John, C.T., Staubitz, T., Meinel, C.: Took a MOOC. Got a Certificate. What now?2019 IEEE Frontiers in Education Conference (FIE) (2019).
John, C.T., Staubitz, T., Meinel, C.: Performance of Men and Women in Graded Team Assignments in MOOCs.2019 Learning With MOOCS (LWMOOCS) (2019).
Traifeh, H., Staubitz, T., Meinel, C.: Improving learner experience and participation in MOOCs: A design thinking approach.2019 Learning With MOOCS (LWMOOCS) (2019).
Staubitz, T., Meinel, C.: Graded Team Assignments in MOOCs: Effects of Team Composition and Further Factors on Team Dropout Rates and Performance.Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale. p. 5:1--5:10. ACM, Chicago, IL, USA (2019).
The ability to work in teams is an important skill in today's work environments. In MOOCs, however, team work, team tasks, and graded team-based assignments play only a marginal role. To close this gap, we have been exploring ways to integrate graded team-based assignments in MOOCs. Some goals of our work are to determine simple criteria to match teams in a volatile environment and to enable a frictionless online collaboration for the participants within our MOOC platform. The high dropout rates in MOOCs pose particular challenges for team work in this context. By now, we have conducted 15 MOOCs containing graded team-based assignments in a variety of topics. The paper at hand presents a study that aims to establish a solid understanding of the participants in the team tasks. Furthermore, we attempt to determine which team compositions are particularly successful. Finally, we examine how several modifications to our platform's collaborative toolset have affected the dropout rates and performance of the teams.
Further Information
AbstractThe ability to work in teams is an important skill in today's work environments. In MOOCs, however, team work, team tasks, and graded team-based assignments play only a marginal role. To close this gap, we have been exploring ways to integrate graded team-based assignments in MOOCs. Some goals of our work are to determine simple criteria to match teams in a volatile environment and to enable a frictionless online collaboration for the participants within our MOOC platform. The high dropout rates in MOOCs pose particular challenges for team work in this context. By now, we have conducted 15 MOOCs containing graded team-based assignments in a variety of topics. The paper at hand presents a study that aims to establish a solid understanding of the participants in the team tasks. Furthermore, we attempt to determine which team compositions are particularly successful. Finally, we examine how several modifications to our platform's collaborative toolset have affected the dropout rates and performance of the teams.
Staubitz, T., Teusner, R., Meinel, C.: MOOCs in Secondary Education - Experiments and Observations from German Classrooms.2019 IEEE Global Engineering Education Conference (EDUCON). pp. 173-182 (2019).
Computer science education in German schools is often less than optimal. It is only mandatory in a few of the federal states and there is a lack of qualified teachers. As a MOOC (Massive Open Online Course) provider with a German background, we developed the idea to implement a MOOC addressing pupils in secondary schools to fill this gap. The course targeted high school pupils and enabled them to learn the Python programming language. In 2014, we successfully conducted the first iteration of this MOOC with more than 7000 participants. However, the share of pupils in the course was not quite satisfactory. So we conducted several workshops with teachers to find out why they had not used the course to the extent that we had imagined. The paper at hand explores and discusses the steps we have taken in the following years as a result of these workshops.
Further Information
AbstractComputer science education in German schools is often less than optimal. It is only mandatory in a few of the federal states and there is a lack of qualified teachers. As a MOOC (Massive Open Online Course) provider with a German background, we developed the idea to implement a MOOC addressing pupils in secondary schools to fill this gap. The course targeted high school pupils and enabled them to learn the Python programming language. In 2014, we successfully conducted the first iteration of this MOOC with more than 7000 participants. However, the share of pupils in the course was not quite satisfactory. So we conducted several workshops with teachers to find out why they had not used the course to the extent that we had imagined. The paper at hand explores and discusses the steps we have taken in the following years as a result of these workshops.
Bothe, M., Renz, J., Rohloff, T., Meinel, C.: From MOOCs to Micro Learning Activities.2019 IEEE Global Engineering Education Conference (EDUCON). pp. 280-288 (2019).
Mobile devices are omnipresent in our daily lives. They are utilized for a variety of tasks and used multiple times for short periods throughout the day. MOOC providers optimized their platforms for these devices in order to support ubiquitous learning. While a combination of desktop and mobile learning yields improved course performances, standalone learning on mobile devices does not perform in the same manner. One indicator for this is the mismatch between the average usage pattern of mobile devices and the time to consume one content item in a MOOC. Micro learning builds on bite-sized learning material and focusses on short-term learning sessions. This work examines the potential of micro learning activities in the context of MOOCs. Therefore, a framework for video-based micro learning is presented, which features a personalized curriculum. Videos are suggested to the user in a non-linear order that is determined by content dependencies, users’ preferences and watched videos, as well as explicit and implicit user feedback. A mobile application was implemented to test the approach with restructured MOOC content resulting in 58 connected short videos about engineering education – e.g. web technologies and programming languages. The usage data indicates initial curiosity by the users. To improve retention rates, more user motivation will be required for future studies. A survey gathered additional qualitative feedback. While the content suggestions were seen as a vital feature for such an approach, the results showed good interest and acceptance rates to create a better learning experience for MOOCs on mobile devices.
Further Information
AbstractMobile devices are omnipresent in our daily lives. They are utilized for a variety of tasks and used multiple times for short periods throughout the day. MOOC providers optimized their platforms for these devices in order to support ubiquitous learning. While a combination of desktop and mobile learning yields improved course performances, standalone learning on mobile devices does not perform in the same manner. One indicator for this is the mismatch between the average usage pattern of mobile devices and the time to consume one content item in a MOOC. Micro learning builds on bite-sized learning material and focusses on short-term learning sessions. This work examines the potential of micro learning activities in the context of MOOCs. Therefore, a framework for video-based micro learning is presented, which features a personalized curriculum. Videos are suggested to the user in a non-linear order that is determined by content dependencies, users’ preferences and watched videos, as well as explicit and implicit user feedback. A mobile application was implemented to test the approach with restructured MOOC content resulting in 58 connected short videos about engineering education – e.g. web technologies and programming languages. The usage data indicates initial curiosity by the users. To improve retention rates, more user motivation will be required for future studies. A survey gathered additional qualitative feedback. While the content suggestions were seen as a vital feature for such an approach, the results showed good interest and acceptance rates to create a better learning experience for MOOCs on mobile devices.
Bothe, M., Meinel, C.: Applied Mobile-Assisted Seamless Learning Techniques in MOOCs. In: Calise, M., Delgado Kloos, C., Reich, J., Ruiperez-Valiente, J.A., and Wirsing, M. (eds.) Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge. p. 21--30. Springer International Publishing, Cham (2019).
As Massive Open Online Courses (MOOCs) are nowadays used in an increasingly ubiquitous manner, the learning process gets disrupted every time learners change context. Mobile-Assisted Seamless Learning (MSL) techniques have been identified to reduce unwanted overhead for learners and streamline their learning process. However, technical implementations vary across the industry. This paper examines existing MSL research and applied techniques in the context of MOOCs. Therefore, we discussed related MSL research topics. Afterward, eleven characteristic MSL features were selected and compared their implementations across five major MOOC platforms. While web applications provide a bigger feature set, mobile clients offer advanced offline capabilities. Based on the findings, a concept outlines how MSL features can enhance the learning experience on MOOC platforms while considering the technical feasibility.
Further Information
Editor(s)Calise, Mauro and Delgado Kloos, Carlos and Reich, Justin and Ruiperez-Valiente, Jose A. and Wirsing, Martin
AbstractAs Massive Open Online Courses (MOOCs) are nowadays used in an increasingly ubiquitous manner, the learning process gets disrupted every time learners change context. Mobile-Assisted Seamless Learning (MSL) techniques have been identified to reduce unwanted overhead for learners and streamline their learning process. However, technical implementations vary across the industry. This paper examines existing MSL research and applied techniques in the context of MOOCs. Therefore, we discussed related MSL research topics. Afterward, eleven characteristic MSL features were selected and compared their implementations across five major MOOC platforms. While web applications provide a bigger feature set, mobile clients offer advanced offline capabilities. Based on the findings, a concept outlines how MSL features can enhance the learning experience on MOOC platforms while considering the technical feasibility.
von Schmieden, K., Staubitz, T., Mayer, L., Meinel, C.: Skill Confidence Ratings in a MOOC: Examining the Link between Skill Confidence and Learner Development.CSEDU (2019).
Grella, C.T., Staubitz, T., Teusner, R., Meinel, C.: Can MOOCs Support Secondary Education in Computer Science? In: Auer, M.E., Guralnick, D., and Uhomoibhi, J. (eds.) Interactive Collaborative Learning. p. 478--493. Springer International Publishing, Cham (2017).
Despite the importance of competencies in computer science for participation in the digital transformation of nearly all sectors, there is still a lack of learning material and technically experienced teachers in German schools. In the paper at hand, we investigate the potential of Massive Open Online Courses (MOOCs) for secondary education. Schools can profit from this learning content and format provided by well-known institutions. However, German schools provide some challenging conditions, which have to be taken into account for a meaningful integration of e-learning elements. Our statistical and qualitative results are based on the representative data of the National Educational Panel Study (NEPS), the learning data of more than 100,000 online learners from over 150 countries, and the outcomes of several workshops with teachers and school administrators.
Further Information
Editor(s)Auer, Michael E. and Guralnick, David and Uhomoibhi, James
AbstractDespite the importance of competencies in computer science for participation in the digital transformation of nearly all sectors, there is still a lack of learning material and technically experienced teachers in German schools. In the paper at hand, we investigate the potential of Massive Open Online Courses (MOOCs) for secondary education. Schools can profit from this learning content and format provided by well-known institutions. However, German schools provide some challenging conditions, which have to be taken into account for a meaningful integration of e-learning elements. Our statistical and qualitative results are based on the representative data of the National Educational Panel Study (NEPS), the learning data of more than 100,000 online learners from over 150 countries, and the outcomes of several workshops with teachers and school administrators.
Malchow, M., Renz, J., Bauer, M., Meinel, C.: Embedded Smart Home - Remote Lab Grading in a MOOC with over 6000 Participants.2017 Annual IEEE Systems Conference (SysCon). IEEE (2017).
The popularity of MOOCs has increased considerably in the last years. A typical MOOC course consists of video content, self tests after a video and homework, which is normally in multiple choice format. After solving this homeworks for every week of a MOOC, the final exam certificate can be issued when the student has reached a sufficient score. There are also some attempts to include practical tasks, such as programming, in MOOCs for grading. Nevertheless, until now there is no known possibility to teach embedded system programming in a MOOC course where the programming can be done in a remote lab and where grading of the tasks is additionally possible. This embedded programming includes communication over GPIO pins to control LEDs and measure sensor values. We started a MOOC course called ``Embedded Smart Home'' as a pilot to prove the concept to teach real hardware programming in a MOOC environment under real life MOOC conditions with over 6000 students. Furthermore, also students with real hardware have the possibility to program on their own real hardware and grade their results in the MOOC course. Finally, we evaluate our approach and analyze the student acceptance of this approach to offer a course on embedded programming. We also analyze the hardware usage and working time of students solving tasks to find out if real hardware programming is an advantage and motivating achievement to support students learning success.
Further Information
AbstractThe popularity of MOOCs has increased considerably in the last years. A typical MOOC course consists of video content, self tests after a video and homework, which is normally in multiple choice format. After solving this homeworks for every week of a MOOC, the final exam certificate can be issued when the student has reached a sufficient score. There are also some attempts to include practical tasks, such as programming, in MOOCs for grading. Nevertheless, until now there is no known possibility to teach embedded system programming in a MOOC course where the programming can be done in a remote lab and where grading of the tasks is additionally possible. This embedded programming includes communication over GPIO pins to control LEDs and measure sensor values. We started a MOOC course called ``Embedded Smart Home'' as a pilot to prove the concept to teach real hardware programming in a MOOC environment under real life MOOC conditions with over 6000 students. Furthermore, also students with real hardware have the possibility to program on their own real hardware and grade their results in the MOOC course. Finally, we evaluate our approach and analyze the student acceptance of this approach to offer a course on embedded programming. We also analyze the hardware usage and working time of students solving tasks to find out if real hardware programming is an advantage and motivating achievement to support students learning success.
Staubitz, T., Teusner, R., Meinel, C.: Towards a Repository for Open Auto-Gradable Programming Exercises.2017 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (2017).
Auto-gradable hands-on programming exercises are a key element for scalable programming courses. A variety of auto-graders already exist, however, creating suitable high- quality exercises in a sufficient amount is a very time-consuming and tedious task. One way to approach this problem is to enable sharing auto-gradable exercises between several interested parties. School-teachers, MOOC1 instructors, workshop providers, and university level teachers need programming exercises to provide their students with hands-on experience. Auto-gradability of these exercises is an important requirement. The paper at hand introduces a tool that enables the sharing of such exercises and addresses the various needs and requirements of the different stakeholders.
Further Information
AbstractAuto-gradable hands-on programming exercises are a key element for scalable programming courses. A variety of auto-graders already exist, however, creating suitable high- quality exercises in a sufficient amount is a very time-consuming and tedious task. One way to approach this problem is to enable sharing auto-gradable exercises between several interested parties. School-teachers, MOOC1 instructors, workshop providers, and university level teachers need programming exercises to provide their students with hands-on experience. Auto-gradability of these exercises is an important requirement. The paper at hand introduces a tool that enables the sharing of such exercises and addresses the various needs and requirements of the different stakeholders.
Staubitz, T., Meinel, C.: Collaboration and Teamwork on a MOOC Platform: A Toolset.Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale. p. 165--168. ACM, Cambridge, Massachusetts, USA (2017).
Teamwork is an an important topic in education. It fosters deep learning and allows educators to assign interesting tasks, which would be too complex to be solved by single participants due to the time restrictions defined by the context of a course.Furthermore, today's jobs require an increasing amount of team skills. On the other hand, teamwork comes with a variety of issues of its own. Particularly in large scale settings, such as MOOCs, teamwork is challenging. Courses often end with dysfunctional teams due to drop-outs or insufficient matching. The paper at hand presents a set of three tools that we have recently added to our system to enable teamwork in our courses. This toolset consists of the TeamBuilder, a tool to match successful teams based on a variable set of parameters, CollabSpaces, providing teams with a secluded area to communicate and collaborate within the course context, and a TeamPeerAssessment tool, which allows to provide teams with complex tasks and which allows assessment that sufficiently scales for the MOOC context. The presented tools are evaluated in terms of success rates of the created teams and workload reduction for the courses' teaching teams.
Further Information
AbstractTeamwork is an an important topic in education. It fosters deep learning and allows educators to assign interesting tasks, which would be too complex to be solved by single participants due to the time restrictions defined by the context of a course.Furthermore, today's jobs require an increasing amount of team skills. On the other hand, teamwork comes with a variety of issues of its own. Particularly in large scale settings, such as MOOCs, teamwork is challenging. Courses often end with dysfunctional teams due to drop-outs or insufficient matching. The paper at hand presents a set of three tools that we have recently added to our system to enable teamwork in our courses. This toolset consists of the TeamBuilder, a tool to match successful teams based on a variable set of parameters, CollabSpaces, providing teams with a secluded area to communicate and collaborate within the course context, and a TeamPeerAssessment tool, which allows to provide teams with complex tasks and which allows assessment that sufficiently scales for the MOOC context. The presented tools are evaluated in terms of success rates of the created teams and workload reduction for the courses' teaching teams.
Staubitz, T., Willems, C., Hagedorn, C., Meinel, C.: The gamification of a MOOC platform.2017 IEEE Global Engineering Education Conference (EDUCON). pp. 883-892 (2017).
Massive Open Online Courses (MOOCs) have left their mark on the face of education during the recent couple of years. At the Hasso Plattner Institute (HPI) in Potsdam, Germany, we are actively developing a MOOC platform, which provides our research with a plethora of e-learning topics, such as learning analytics, automated assessment, peer assessment, team-work, online proctoring, and gamification. We run several instances of this platform. On openHPI, we provide our own courses from within the HPI context. Further instances are openSAP, openWHO, and mooc.HOUSE, which is the smallest of these platforms, targeting customers with a less extensive course portfolio. In 2013, we started to work on the gamification of our platform. By now, we have implemented about two thirds of the features that we initially have evaluated as useful for our purposes. About a year ago we activated the implemented gamification features on mooc.HOUSE. They have been employed actively in the course “Design for Non-Designers”. We plan to activate the features on openHPI in the beginning of 2017. The paper at hand recaps, examines, and re-evaluates our initial recommendations.
Further Information
AbstractMassive Open Online Courses (MOOCs) have left their mark on the face of education during the recent couple of years. At the Hasso Plattner Institute (HPI) in Potsdam, Germany, we are actively developing a MOOC platform, which provides our research with a plethora of e-learning topics, such as learning analytics, automated assessment, peer assessment, team-work, online proctoring, and gamification. We run several instances of this platform. On openHPI, we provide our own courses from within the HPI context. Further instances are openSAP, openWHO, and mooc.HOUSE, which is the smallest of these platforms, targeting customers with a less extensive course portfolio. In 2013, we started to work on the gamification of our platform. By now, we have implemented about two thirds of the features that we initially have evaluated as useful for our purposes. About a year ago we activated the implemented gamification features on mooc.HOUSE. They have been employed actively in the course “Design for Non-Designers”. We plan to activate the features on openHPI in the beginning of 2017. The paper at hand recaps, examines, and re-evaluates our initial recommendations.
Staubitz, T., Teusner, R., Meinel, C.: openHPI's Coding Tool Family: CodeOcean, CodeHarbor, CodePilot.Automatische Bewertung von Programmieraufgaben (ABP) (2017).
The Hasso Plattner Institute successfully runs a self-developed Massive Open Online Course (MOOC) platform—openHPI—since 2012. MOOCs, even more than classic classroom situations, depend on automated solutions to assess programming exercises. Manual evaluation is not an option due to the massive amount of users that participate in these courses. The paper at hand maps the landscape of tools that are used on openHPI in the context of automated grading of programming exercises. Furthermore, it provides a sneak preview to new features that will be integrated ion the near future. Particularly, we will introduce CodeHarbor, our platform to share auto-gradeable exercises between various online code execution platforms.
Further Information
AbstractThe Hasso Plattner Institute successfully runs a self-developed Massive Open Online Course (MOOC) platform—openHPI—since 2012. MOOCs, even more than classic classroom situations, depend on automated solutions to assess programming exercises. Manual evaluation is not an option due to the massive amount of users that participate in these courses. The paper at hand maps the landscape of tools that are used on openHPI in the context of automated grading of programming exercises. Furthermore, it provides a sneak preview to new features that will be integrated ion the near future. Particularly, we will introduce CodeHarbor, our platform to share auto-gradeable exercises between various online code execution platforms.
Malchow, M., Renz, J., Bauer, M., Meinel, C.: Improved E-learning Experience with Embedded LED System.2016 Annual IEEE Systems Conference (SysCon). IEEE (2016).
During the last years, e-learning has become more and more important. There are several approaches like teleteaching or MOOCs to delivers knowledge information to the students on different topics. But, a major problem most learning platforms have is, students often get demotivated fast. This is caused e.g. by solving similar tasks again and again, and learning alone on the personal computer. To avoid this situation in coding-based courses one possible way could be the use of embedded devices. This approach increases the practical programming part and should push motivation to the students. This paper presents a possibility to the use of embedded systems with an LED panel to motivate students to use programming languages and solve the course successfully. To analyze the successfulness of this approach, it was tested within a MOOC called "Java for beginners" with 11,712 participants. The result was evaluated by personal feedback of the students and user data was analyzed to measure the acceptance and motivation of students by solving the embedded system tasks. The result shows that the approach is well accepted by the students and they are more motivated by tasks with real hardware support.
Further Information
AbstractDuring the last years, e-learning has become more and more important. There are several approaches like teleteaching or MOOCs to delivers knowledge information to the students on different topics. But, a major problem most learning platforms have is, students often get demotivated fast. This is caused e.g. by solving similar tasks again and again, and learning alone on the personal computer. To avoid this situation in coding-based courses one possible way could be the use of embedded devices. This approach increases the practical programming part and should push motivation to the students. This paper presents a possibility to the use of embedded systems with an LED panel to motivate students to use programming languages and solve the course successfully. To analyze the successfulness of this approach, it was tested within a MOOC called "Java for beginners" with 11,712 participants. The result was evaluated by personal feedback of the students and user data was analyzed to measure the acceptance and motivation of students by solving the embedded system tasks. The result shows that the approach is well accepted by the students and they are more motivated by tasks with real hardware support.
Malchow, M., Bauer, M., Meinel, C.: Couch Learning Mode: Multiple-Video Lecture Playlist Selection out of a Lecture Video Archive for E-learning Students.Proceedings of the 2016 ACM on SIGUCCS Annual Conference. pp. 77-82. ACM (2016).
During a video recorded university class students have to watch several hours of video content. This can easily add up to several days of video content during a semester. Naturally, not all 90 minutes of a typical lecture are relevant for the exam. When the semester ends with a final exam students have to study more intensively the important parts of all the lectures. To simplify the learning process and design it to be more efficient we have introduced the Couch Learning Mode in our lecture video archive. With this approach students can create custom playlists out of the video lecture archive with a time frame for every selected video. Finally, students can lean back and watch all relevant video parts consecutively for the exam without being interrupted. Additionally, the students can share their playlists with other students or they can use the video search to watch all relevant lecture videos about a topic. This approach uses playlists and HTML5 technologies to realize the consecutive video playback. Furthermore, the powerful Lecture Butler search engine is used to find worthwhile video parts for certain topics. Our approach shows that we have more satisfied students using the manual playlist creation to view reasonable parts for an exam. Finally, students are keen on watching the top search results showing reasonable parts of lectures for a topic of interest. The Couch Learning Mode supports and motivates students to learn with video lectures for an exam and daily life.
Further Information
AbstractDuring a video recorded university class students have to watch several hours of video content. This can easily add up to several days of video content during a semester. Naturally, not all 90 minutes of a typical lecture are relevant for the exam. When the semester ends with a final exam students have to study more intensively the important parts of all the lectures. To simplify the learning process and design it to be more efficient we have introduced the Couch Learning Mode in our lecture video archive. With this approach students can create custom playlists out of the video lecture archive with a time frame for every selected video. Finally, students can lean back and watch all relevant video parts consecutively for the exam without being interrupted. Additionally, the students can share their playlists with other students or they can use the video search to watch all relevant lecture videos about a topic. This approach uses playlists and HTML5 technologies to realize the consecutive video playback. Furthermore, the powerful Lecture Butler search engine is used to find worthwhile video parts for certain topics. Our approach shows that we have more satisfied students using the manual playlist creation to view reasonable parts for an exam. Finally, students are keen on watching the top search results showing reasonable parts of lectures for a topic of interest. The Couch Learning Mode supports and motivates students to learn with video lectures for an exam and daily life.
Staubitz, T., Petrick, D., Bauer, M., Renz, J., Meinel, C.: Improving the Peer Assessment Experience on MOOC Platforms.Proceedings of ACM Learning at Scale Conference (L@S2016). ACM (2016).
Massive Open Online Courses (MOOCs) have revolutionized higher education by offering university-like courses for a large amount of learners via the Internet. The paper at hand takes a closer look on peer assessment as a tool for delivering individualized feedback and engaging assignments to MOOC participants. Benefits, such as scalability for MOOCs and higher order learning, and challenges, such as grading accuracy and rogue reviewers, are described. Common practices and the state-of-the-art to counteract challenges are highlighted. Based on this research, the paper at hand describes a peer assessment workflow and its implementation on the openHPI and openSAP MOOC platforms. This workflow combines the best practices of existing peer assessment tools and introduces some small but crucial improvements.
Further Information
AbstractMassive Open Online Courses (MOOCs) have revolutionized higher education by offering university-like courses for a large amount of learners via the Internet. The paper at hand takes a closer look on peer assessment as a tool for delivering individualized feedback and engaging assignments to MOOC participants. Benefits, such as scalability for MOOCs and higher order learning, and challenges, such as grading accuracy and rogue reviewers, are described. Common practices and the state-of-the-art to counteract challenges are highlighted. Based on this research, the paper at hand describes a peer assessment workflow and its implementation on the openHPI and openSAP MOOC platforms. This workflow combines the best practices of existing peer assessment tools and introduces some small but crucial improvements.
Staubitz, T., Teusner, R., Renz, J., Meinel, C.: First Steps in Automated Proctoring.Proceedings of the Fourth MOOC European Stakeholders Summit (EMOOCs 2016). P.A.U (2016).
Bauer, M., Malchow, M., Staubitz, T., Meinel, C.: Improving Collaborative Learning With Video Lectures.INTED2016 Proceedings. 10th International Technology, Education and Development ConferenceValencia, Spain. 7-9 March, 2016. pp. 5511-5517. IATED (2016).
We have addressed the problems of independent e-lecture learning with an approach involving collaborative learning with lecture recordings. In order to make this type of learning possible, we have prototypically enhanced the video player of a lecture video platform with functionality that allows simultaneous viewing of a lecture on two or more computers. While watching the video, synchronization of the playback and every click event, such as play, pause, seek, and playback speed adjustment can be carried out. We have also added the option of annotating slides. With this approach, it is possible for learners to watch a lecture together, even though they are in different places. In this way, the benefits of collaborative learning can also be used when learning online. Now, it is more likely that learners stay focused on the lecture for a longer time (as the collaboration creates an additional obligation not to leave early and desert a friend). Furthermore, the learning outcome is higher because learners can ask their friends questions and explain things to each other as well as mark important points in the lecture video.
Further Information
AbstractWe have addressed the problems of independent e-lecture learning with an approach involving collaborative learning with lecture recordings. In order to make this type of learning possible, we have prototypically enhanced the video player of a lecture video platform with functionality that allows simultaneous viewing of a lecture on two or more computers. While watching the video, synchronization of the playback and every click event, such as play, pause, seek, and playback speed adjustment can be carried out. We have also added the option of annotating slides. With this approach, it is possible for learners to watch a lecture together, even though they are in different places. In this way, the benefits of collaborative learning can also be used when learning online. Now, it is more likely that learners stay focused on the lecture for a longer time (as the collaboration creates an additional obligation not to leave early and desert a friend). Furthermore, the learning outcome is higher because learners can ask their friends questions and explain things to each other as well as mark important points in the lecture video.
Che, X., Staubitz, T., Yang, H., Meinel, C.: Pre-Course Key Segment Analysis of Online Lecture Videos.Proceedings of The 16th IEEE International Conference on Advanced Learning Technology (ICALT2016). , Austin, Texas, USA (2016).
In this paper we propose a method to evaluate the importance of lecture video segments in online courses. The video will be first segmented based on the slide transition. Then we evaluate the importance of each segment based on our analysis of the teacher’s focus. This focus is mainly identified by exploring features in the slide and the speech. Since the whole analysis process is based on multimedia materials, it could be done before the official start of the course. By setting survey questions and collecting forum statistics in the MOOC “Web Technologies”, the proposed method is evaluated. Both the general trend and the high accuracy of selected key segments (over 70%) prove the effectiveness of the proposed method.
Further Information
AbstractIn this paper we propose a method to evaluate the importance of lecture video segments in online courses. The video will be first segmented based on the slide transition. Then we evaluate the importance of each segment based on our analysis of the teacher’s focus. This focus is mainly identified by exploring features in the slide and the speech. Since the whole analysis process is based on multimedia materials, it could be done before the official start of the course. By setting survey questions and collecting forum statistics in the MOOC “Web Technologies”, the proposed method is evaluated. Both the general trend and the high accuracy of selected key segments (over 70%) prove the effectiveness of the proposed method.
Staubitz, T., Klement, H., Teusner, R., Renz, J., Meinel, C.: CodeOcean - A Versatile Platform for Practical Programming Excercises in Online Environments.Proceedings of IEEE Global Engineering Education Conference (EDUCON2016). IEEE (2016).
Staubitz, T., Brehm, M., Jasper, J., Werkmeister, T., Teusner, R., Willems, C., Renz, J., Meinel, C.: Vagrant Virtual Machines for Hands-On Exercises in Massive Open Online Courses.Smart Education and e-Learning 2016. p. 363--373. Springer International Publishing (2016).
In many MOOCs hands-on exercises are a key component. Their format must be deliberately planned to satisfy the needs of a more and more heterogeneous student body. At the same time, costs have to be kept low for maintenance and support on the course provider’s side. The paper at hand reports about our experiments with a tool called Vagrant in this context. It has been successfully employed for use cases similar to ours and thus promises to be an option for achieving our goals.
Further Information
AbstractIn many MOOCs hands-on exercises are a key component. Their format must be deliberately planned to satisfy the needs of a more and more heterogeneous student body. At the same time, costs have to be kept low for maintenance and support on the course provider’s side. The paper at hand reports about our experiments with a tool called Vagrant in this context. It has been successfully employed for use cases similar to ours and thus promises to be an option for achieving our goals.
Malchow, M., Renz, J., Bauer, M., Meinel, C.: Enhance Embedded System E-learning Experience with Sensors.2016 IEEE Global Engineering Education Conference (EDUCON). pp. 175-183. IEEE (2016).
Earlier research shows that using an embedded LED system motivates students to learn programming languages in massive open online courses (MOOCs) efficiently. Since this earlier approach was very successful the system should be improved to increase the learning experience for students during programming exercises. The problem of the current system is that only a static image was shown on the LED matrix controlled by students’ array programming over the embedded system. The idea of this paper to change this static behavior into a dynamic display of information on the LED matrix by the use of sensors which are connected with the embedded system. For this approach a light sensor and a temperature sensor are connected to an analog-to-digital converter (ADC) port of the embedded system. These sensors' values can be read by the students to compute the correct output for the LED matrix. The result is captured and sent back to the students for direct feedback. Furthermore, unit tests can be used to automatically evaluate the programming results. The system was evaluated during a MOOC course about web technologies using JavaScript. Evaluation results are taken from the student’s feedback and an evaluation of the students’ code executions on the system. The positive feedback and the evaluation of the students’ executions, which shows a higher amount of code executions compared to standard programming tasks and the fact that students solving these tasks have overall better course results, highlight the advantage of the approach. Due to the evaluation results, this approach should be used in e-learning e.g. MOOCs teaching programming languages to increase the learning experience and motivate students to learn programming.
Further Information
AbstractEarlier research shows that using an embedded LED system motivates students to learn programming languages in massive open online courses (MOOCs) efficiently. Since this earlier approach was very successful the system should be improved to increase the learning experience for students during programming exercises. The problem of the current system is that only a static image was shown on the LED matrix controlled by students’ array programming over the embedded system. The idea of this paper to change this static behavior into a dynamic display of information on the LED matrix by the use of sensors which are connected with the embedded system. For this approach a light sensor and a temperature sensor are connected to an analog-to-digital converter (ADC) port of the embedded system. These sensors' values can be read by the students to compute the correct output for the LED matrix. The result is captured and sent back to the students for direct feedback. Furthermore, unit tests can be used to automatically evaluate the programming results. The system was evaluated during a MOOC course about web technologies using JavaScript. Evaluation results are taken from the student’s feedback and an evaluation of the students’ code executions on the system. The positive feedback and the evaluation of the students’ executions, which shows a higher amount of code executions compared to standard programming tasks and the fact that students solving these tasks have overall better course results, highlight the advantage of the approach. Due to the evaluation results, this approach should be used in e-learning e.g. MOOCs teaching programming languages to increase the learning experience and motivate students to learn programming.
Bauer, M., Malchow, M., Staubitz, T., Meinel, C.: Improving Collaborative Learning With Video Lectures.INTED2016 Proceedings. 10th International Technology, Education and Development Conference Valencia, Spain. 7-9 March, 2016. pp. 5511-5517. IATED (2016).
We have addressed the problems of independent e-lecture learning with an approach involving collaborative learning with lecture recordings. In order to make this type of learning possible, we have prototypically enhanced the video player of a lecture video platform with functionality that allows simultaneous viewing of a lecture on two or more computers. While watching the video, synchronization of the playback and every click event, such as play, pause, seek, and playback speed adjustment can be carried out. We have also added the option of annotating slides. With this approach, it is possible for learners to watch a lecture together, even though they are in different places. In this way, the benefits of collaborative learning can also be used when learning online. Now, it is more likely that learners stay focused on the lecture for a longer time (as the collaboration creates an additional obligation not to leave early and desert a friend). Furthermore, the learning outcome is higher because learners can ask their friends questions and explain things to each other as well as mark important points in the lecture video.
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AbstractWe have addressed the problems of independent e-lecture learning with an approach involving collaborative learning with lecture recordings. In order to make this type of learning possible, we have prototypically enhanced the video player of a lecture video platform with functionality that allows simultaneous viewing of a lecture on two or more computers. While watching the video, synchronization of the playback and every click event, such as play, pause, seek, and playback speed adjustment can be carried out. We have also added the option of annotating slides. With this approach, it is possible for learners to watch a lecture together, even though they are in different places. In this way, the benefits of collaborative learning can also be used when learning online. Now, it is more likely that learners stay focused on the lecture for a longer time (as the collaboration creates an additional obligation not to leave early and desert a friend). Furthermore, the learning outcome is higher because learners can ask their friends questions and explain things to each other as well as mark important points in the lecture video.
Che, X., Wang, C., Yang, H., Meinel, C.: Punctuation Prediction for Unsegmented Transcript Based on Word Vector.Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). pp. 654-658. , Portorož, Slovenia (2016).
In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training model of Deep Neural Network (DNN) or Convolutional Neural Network (CNN) is applied to classify whether a punctuation mark should be inserted after the third word of a 5-words sequence and which kind of punctuation mark the inserted one should be. TED talks within IWSLT dataset are used in both training and evaluation phases. The proposed approach shows its effectiveness by achieving better result than the state-of-the-art lexical solution which works with same type of data, especially when predicting puncuation position only.
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AbstractIn this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training model of Deep Neural Network (DNN) or Convolutional Neural Network (CNN) is applied to classify whether a punctuation mark should be inserted after the third word of a 5-words sequence and which kind of punctuation mark the inserted one should be. TED talks within IWSLT dataset are used in both training and evaluation phases. The proposed approach shows its effectiveness by achieving better result than the state-of-the-art lexical solution which works with same type of data, especially when predicting puncuation position only.
Che, X., Luo, S., Yang, H., Meinel, C.: Sentence Boundary Detection Based on Parallel Lexical and Acoustic Models.Proceedings of Interspeech 2016. pp. 257-261. , San Francisco, CA, USA (2016).
In this paper we propose a solution that detects sentence boundary from speech transcript. First we train a pure lexical model with deep neural network, which takes word vectors as the only input feature. Then a simple acoustic model is also prepared. Because the models work independently, they can be trained with different data. In next step, the posterior probabilities of both lexical and acoustic models will be involved in a heuristic 2-stage joint decision scheme to classify the sentence boundary positions. This approach ensures that the models can be updated or switched freely in actual use. Evaluation on TED Talks shows that the proposed lexical model can achieve good results: 75.5% accuracy on error-involved ASR transcripts and 82.4% on error-free manual references. The joint decision scheme can further improve the accuracy by 3�~10% when acoustic data is available.
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AbstractIn this paper we propose a solution that detects sentence boundary from speech transcript. First we train a pure lexical model with deep neural network, which takes word vectors as the only input feature. Then a simple acoustic model is also prepared. Because the models work independently, they can be trained with different data. In next step, the posterior probabilities of both lexical and acoustic models will be involved in a heuristic 2-stage joint decision scheme to classify the sentence boundary positions. This approach ensures that the models can be updated or switched freely in actual use. Evaluation on TED Talks shows that the proposed lexical model can achieve good results: 75.5% accuracy on error-involved ASR transcripts and 82.4% on error-free manual references. The joint decision scheme can further improve the accuracy by 3�~10% when acoustic data is available.
Staubitz, T., Petrick, D., Bauer, M., Renz, J., Meinel, C.: Improving the Peer Assessment Experience on MOOC Platforms.Proceedings of ACM Learning at Scale Conference (L@S2016). ACM (2016).
Massive Open Online Courses (MOOCs) have revolutionized higher education by offering university-like courses for a large amount of learners via the Internet. The paper at hand takes a closer look on peer assessment as a tool for delivering individualized feedback and engaging assignments to MOOC participants. Benefits, such as scalability for MOOCs and higher order learning, and challenges, such as grading accuracy and rogue reviewers, are described. Common practices and the state-of-the-art to counteract challenges are highlighted. Based on this research, the paper at hand describes a peer assessment workflow and its implementation on the openHPI and openSAP MOOC platforms. This workflow combines the best practices of existing peer assessment tools and introduces some small but crucial improvements.
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AbstractMassive Open Online Courses (MOOCs) have revolutionized higher education by offering university-like courses for a large amount of learners via the Internet. The paper at hand takes a closer look on peer assessment as a tool for delivering individualized feedback and engaging assignments to MOOC participants. Benefits, such as scalability for MOOCs and higher order learning, and challenges, such as grading accuracy and rogue reviewers, are described. Common practices and the state-of-the-art to counteract challenges are highlighted. Based on this research, the paper at hand describes a peer assessment workflow and its implementation on the openHPI and openSAP MOOC platforms. This workflow combines the best practices of existing peer assessment tools and introduces some small but crucial improvements.
Luo, S., Yang, H., Wang, C., Che, X., Meinel, C.: Real-time action recognition in surveillance videos using ConvNets.International Conference on Neural Information Processing. pp. 529-537. Springer (2016).
The explosive growth of surveillance cameras and its 7 * 24 recording period brings massive surveillance videos data. Therefore how to efficiently retrieve the rare but important event information inside the videos is eager to be solved. Recently deep convolutinal networks shows its outstanding performance in event recognition on general videos. Hence we study the characteristic of surveillance video context and propose a very competitive ConvNets approach for real-time event recognition on surveillance videos. Our approach adopts two-steam ConvNets to respectively recognition spatial and temporal information of one action. In particular, we propose to use fast feature cascades and motion history image as the template of spatial and temporal stream. We conducted our experiments on UCF-ARG and UT-interaction dataset. The experimental results show that our approach acquires superior recognition accuracy and runs in real-time.
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AbstractThe explosive growth of surveillance cameras and its 7 * 24 recording period brings massive surveillance videos data. Therefore how to efficiently retrieve the rare but important event information inside the videos is eager to be solved. Recently deep convolutinal networks shows its outstanding performance in event recognition on general videos. Hence we study the characteristic of surveillance video context and propose a very competitive ConvNets approach for real-time event recognition on surveillance videos. Our approach adopts two-steam ConvNets to respectively recognition spatial and temporal information of one action. In particular, we propose to use fast feature cascades and motion history image as the template of spatial and temporal stream. We conducted our experiments on UCF-ARG and UT-interaction dataset. The experimental results show that our approach acquires superior recognition accuracy and runs in real-time.
Staubitz, T., Klement, H., Teusner, R., Renz, J., Meinel, C.: CodeOcean - A Versatile Platform for Practical Programming Excercises in Online Environments.Proceedings of IEEE Global Engineering Education Conference (EDUCON2016). IEEE (2016).
Luo, S., Yang, H., Wang, C., Che, X., Meinel, C.: Action Recognition in Surveillance Video Using ConvNets and Motion History Image.Artificial Neural Networks and Machine Learning – ICANN 2016. pp. 187-195. Springer (2016).
With significant increasing of surveillance cameras, the amount of surveillance videos is growing rapidly. Thereby how to automatically and efficiently recognize semantic actions and events in surveillance videos becomes an important problem to be addressed. In this paper, we investigate the state-of-the-art Deep Learning (DL) approaches for human action recognition, and propose an improved two-stream ConvNets architecture for this task. In particular, we propose to use Motion History Image (MHI) as motion expression for training the temporal ConvNet, which achieved impressive results in both accuracy and recognition speed. In our experiment, we conducted an in-depth study to investigate important network options and compared to the latest deep network for action recognition. The detailed evaluation results show the superior ability of our proposed approach, which achieves state-of-the-art in surveillance video context.
Further Information
AbstractWith significant increasing of surveillance cameras, the amount of surveillance videos is growing rapidly. Thereby how to automatically and efficiently recognize semantic actions and events in surveillance videos becomes an important problem to be addressed. In this paper, we investigate the state-of-the-art Deep Learning (DL) approaches for human action recognition, and propose an improved two-stream ConvNets architecture for this task. In particular, we propose to use Motion History Image (MHI) as motion expression for training the temporal ConvNet, which achieved impressive results in both accuracy and recognition speed. In our experiment, we conducted an in-depth study to investigate important network options and compared to the latest deep network for action recognition. The detailed evaluation results show the superior ability of our proposed approach, which achieves state-of-the-art in surveillance video context.
Che, X., Staubitz, T., Yang, H., Meinel, C.: Pre-Course Key Segment Analysis of Online Lecture Videos.Proceedings of The 16th IEEE International Conference on Advanced Learning Technology (ICALT2016). , Austin, Texas, USA (2016).
In this paper we propose a method to evaluate the importance of lecture video segments in online courses. The video will be first segmented based on the slide transition. Then we evaluate the importance of each segment based on our analysis of the teacher’s focus. This focus is mainly identified by exploring features in the slide and the speech. Since the whole analysis process is based on multimedia materials, it could be done before the official start of the course. By setting survey questions and collecting forum statistics in the MOOC “Web Technologies”, the proposed method is evaluated. Both the general trend and the high accuracy of selected key segments (over 70%) prove the effectiveness of the proposed method.
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AbstractIn this paper we propose a method to evaluate the importance of lecture video segments in online courses. The video will be first segmented based on the slide transition. Then we evaluate the importance of each segment based on our analysis of the teacher’s focus. This focus is mainly identified by exploring features in the slide and the speech. Since the whole analysis process is based on multimedia materials, it could be done before the official start of the course. By setting survey questions and collecting forum statistics in the MOOC “Web Technologies”, the proposed method is evaluated. Both the general trend and the high accuracy of selected key segments (over 70%) prove the effectiveness of the proposed method.
Renz, J., Navarro-Suarez, G., Sathi, R., Staubitz, T., Meinel, C.: Enabling Schema Agnostic Learning Analytics in a Service-Oriented MOOC Platform.Proceedings of ACM Learning at Scale Conference (L@S2016). ACM (2016).
Xiaoyin Che, S.L., Meinel, C.: An attempt at mooc localization for chinese-speaking users.International Journal of Information and Education Technology, Volume 6, Number 2. pp. 90-96 (2016).
Abstract—“Internetworking with TCP/IP” is a massive open online course (MOOC) provided by Germany-based MOOC platform “openHPI”, which has been offered in German, English and – recently – Chinese respectively, with similar content. In this paper, the authors, who worked jointly as a teacher (or as teaching assistants) in this course, want to share their ideas derived from daily teaching experiences, analysis of the statistics, comparison between the performance in different language offers and the feedback from user questionnaires. Additionally, the motivation, attempt and suggestion at MOOC localization will also be discussed.
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AbstractAbstract—“Internetworking with TCP/IP” is a massive open online course (MOOC) provided by Germany-based MOOC platform “openHPI”, which has been offered in German, English and – recently – Chinese respectively, with similar content. In this paper, the authors, who worked jointly as a teacher (or as teaching assistants) in this course, want to share their ideas derived from daily teaching experiences, analysis of the statistics, comparison between the performance in different language offers and the feedback from user questionnaires. Additionally, the motivation, attempt and suggestion at MOOC localization will also be discussed.
Staubitz, T., Teusner, R., Renz, J., Meinel, C.: First Steps in Automated Proctoring.Proceedings of the Fourth MOOC European Stakeholders Summit (EMOOCs 2016). P.A.U (2016).
Renz, J., Navarro-Suarez, G., Sathi, R., Staubitz, T., Meinel, C.: Enabling Schema Agnostic Learning Analytics in a Service-Oriented MOOC Platform.Proceedings of ACM Learning at Scale Conference (L@S2016). ACM (2016).
Bauer, M., Malchow, M., Meinel, C.: Enhance Teleteaching Videos with Semantic Technologies. In: Uskov, V.L., Howlett, R.J., and Jain, L.C. (eds.) Smart Education and Smart e-Learning. pp. 105-115. Springer International Publishing (2015).
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Editor(s)Uskov, Vladimir L. and Howlett, Robert J. and Jain, Lakhmi C.
Malchow, M., Bauer, M., Meinel, C.: Self-Test Integration in Lecture Video Archives.ICERI2015 Proceedings - 8th International Conference of Education, Research and Innovation. pp. 7631-7638. IATED (2015).
Lecture video archives offer hundreds of lectures. Students have to watch lecture videos in a lecture archive without any feedback. They do not know if they understood everything correctly in comparison to MOOCs (Massive Open Online Course) where a direct feedback with self-tests or assignments is common. In contrast to MOOCs, video lecture archives normally do not offer self-test or assignment sections after every video. Due to this behavior of lecture archives questions have to be made visible on the video page. Furthermore, lecture recording videos are typically longer than videos in MOOCs. So, it is not so reasonable and sometimes even demotivating to ask a lot of questions after a long video when not all information is already memorized by the student. The approach of this paper is to overcome these self-test problems in lecture video archives and to finally solve them in a reasonable way to increase the learning experience and support students to learn more efficient with recorded lecture videos.
Further Information
AbstractLecture video archives offer hundreds of lectures. Students have to watch lecture videos in a lecture archive without any feedback. They do not know if they understood everything correctly in comparison to MOOCs (Massive Open Online Course) where a direct feedback with self-tests or assignments is common. In contrast to MOOCs, video lecture archives normally do not offer self-test or assignment sections after every video. Due to this behavior of lecture archives questions have to be made visible on the video page. Furthermore, lecture recording videos are typically longer than videos in MOOCs. So, it is not so reasonable and sometimes even demotivating to ask a lot of questions after a long video when not all information is already memorized by the student. The approach of this paper is to overcome these self-test problems in lecture video archives and to finally solve them in a reasonable way to increase the learning experience and support students to learn more efficient with recorded lecture videos.
Malchow, M., Bauer, M., Meinel, C.: Enhance Lecture Archive Search with OCR Slide Detection and In-Memory Database Technology.2015 IEEE 18th International Conference on Computational Science and Engineering (CSE). pp. 176-183. IEEE (2015).
On the Web there are a lot of frequently used video lecture archives which have grown up fast during the last couple of years. This fact led to a lot of lecture recordings which include knowledge for a variety of subjects. The typical way of searching these videos is by title and description. Unfortunately, not all important keywords and facts are mentioned in the title or description if they are available. Furthermore, there is no possibility to analyze how important those detected keywords are for the whole video. Another lecture archive specific virtue is that every regular university lecture is repeated yearly. Normally this will lead to duplicate lecture recordings. In search results doubling is disturbing for students when they want to watch the most recent lectures from the search result. This paper deals with the idea to resolve these problems by analyzing the recorded lecture slides with Optical Character Recognition (OCR). In addition to the name and description the OCR data will be used for a full text analysis to create an index for the lecture archive search. Furthermore, a fuzzy search is introduced. This will solve the issue of misspelled search requests and OCR detection defects. Additionally, this paper deals with the performance issues of a full text search with an in-memory database, issues in OCR detection, handling duplicate recordings of lectures repeated every year. Finally, an evaluation of the search performance in comparison with other database ideas besides the in-memory database is performed. Additionally, a user acceptability survey for the search results to increase the learning experience on lecture archives was performed. As a result, this paper shows how to handle the big amount of OCR data for a full text live search performed on an in-memory database in reasonable time. During this search a fuzzy search is performed additionally to resolve spelling mistakes and OCR detection problems. In conclusion this paper shows a solution for an enhanced video lecture archive search that supports students in online research processes and enhances their learning experience.
Further Information
AbstractOn the Web there are a lot of frequently used video lecture archives which have grown up fast during the last couple of years. This fact led to a lot of lecture recordings which include knowledge for a variety of subjects. The typical way of searching these videos is by title and description. Unfortunately, not all important keywords and facts are mentioned in the title or description if they are available. Furthermore, there is no possibility to analyze how important those detected keywords are for the whole video. Another lecture archive specific virtue is that every regular university lecture is repeated yearly. Normally this will lead to duplicate lecture recordings. In search results doubling is disturbing for students when they want to watch the most recent lectures from the search result. This paper deals with the idea to resolve these problems by analyzing the recorded lecture slides with Optical Character Recognition (OCR). In addition to the name and description the OCR data will be used for a full text analysis to create an index for the lecture archive search. Furthermore, a fuzzy search is introduced. This will solve the issue of misspelled search requests and OCR detection defects. Additionally, this paper deals with the performance issues of a full text search with an in-memory database, issues in OCR detection, handling duplicate recordings of lectures repeated every year. Finally, an evaluation of the search performance in comparison with other database ideas besides the in-memory database is performed. Additionally, a user acceptability survey for the search results to increase the learning experience on lecture archives was performed. As a result, this paper shows how to handle the big amount of OCR data for a full text live search performed on an in-memory database in reasonable time. During this search a fuzzy search is performed additionally to resolve spelling mistakes and OCR detection problems. In conclusion this paper shows a solution for an enhanced video lecture archive search that supports students in online research processes and enhances their learning experience.
Yang, H., Wang, C., Che, X., Luo, S., Meinel, C.: An Improved System For Real-Time Scene Text Recognition.Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (ICMR 2015). pp. 657-660. , Shanghai, China (2015).
In this paper we showcase a system for real-time text detection and recognition. We apply deep features created by Convolutional Neural Networks (CNNs) for both text detection and word recognition task. For text detection we follow the common localization-verification scheme which already shown its excellent ability in numerous previous work. In text localization stage, textual regions are roughly detected by using a MSERs (Maximally Stable Extremal Regions) detector with high recall rate. False alarms are then eliminated by using a CNNs classifier, and remaining text regions are further grouped into words. In the word recognition stage, we developed an skeleton-based text binarization method for segmenting text from its background. A CNNs based recognizer is then applied for recognizing character. The initial experiments show the powerful ability of deep features for text classification comparing with commonly used visual features. Our current implementation demonstrates real-time performance for recognizing scene text by using a standard PC with webcam.
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AbstractIn this paper we showcase a system for real-time text detection and recognition. We apply deep features created by Convolutional Neural Networks (CNNs) for both text detection and word recognition task. For text detection we follow the common localization-verification scheme which already shown its excellent ability in numerous previous work. In text localization stage, textual regions are roughly detected by using a MSERs (Maximally Stable Extremal Regions) detector with high recall rate. False alarms are then eliminated by using a CNNs classifier, and remaining text regions are further grouped into words. In the word recognition stage, we developed an skeleton-based text binarization method for segmenting text from its background. A CNNs based recognizer is then applied for recognizing character. The initial experiments show the powerful ability of deep features for text classification comparing with commonly used visual features. Our current implementation demonstrates real-time performance for recognizing scene text by using a standard PC with webcam.
Staubitz, T., Klement, H., Renz, J., Teusner, R., Meinel, C.: Towards Practical Programming Exercises and Automated Assessment in Massive Open Online Courses.Proceedings of the IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE2015). IEEE, Zhuhai, China (2015).
Che, X., Yang, H., Meinel, C.: Table Detection from Slide Images.Pacific-Rim Symposium on Image and Video Technology. pp. 762-774. , Auckland, New Zealand (2015).
In this paper we propose a solution to detect tables from slide images. Presentation slides are one type of document with growing importance. But the layout difference between slides and traditional documents makes many existing table detection methods less effective on slides. The proposed solution works with both high-resolution slide images from digital files and low-resolution slide screenshots from videos. By taking OCR (Optical Character Recognition) as initial step, a heuristic analysis on page layout focuses not only on the table structure but also the textual content. The evaluation result shows that the proposed solution achieves an approximate accuracy of 80 %. It is way better than the open-source academic solution Tesseract and also outperforms the commercial software ABBYY FineReader, which is supposed to be one of the best table detection tools.
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AbstractIn this paper we propose a solution to detect tables from slide images. Presentation slides are one type of document with growing importance. But the layout difference between slides and traditional documents makes many existing table detection methods less effective on slides. The proposed solution works with both high-resolution slide images from digital files and low-resolution slide screenshots from videos. By taking OCR (Optical Character Recognition) as initial step, a heuristic analysis on page layout focuses not only on the table structure but also the textual content. The evaluation result shows that the proposed solution achieves an approximate accuracy of 80 %. It is way better than the open-source academic solution Tesseract and also outperforms the commercial software ABBYY FineReader, which is supposed to be one of the best table detection tools.
Renz, J., Bauer, M., Malchow, M., Staubitz, T., Meinel, C.: Optimizing the Video Experience in MOOCs.Proceedings of the 7th International Conference on Education and New Learning Technologies (EduLearn). IATED, Barcelona, Spain (2015).
Malchow, M., Bauer, M., Meinel, C.: Lecture Butler - Teaching Reasonable Lectures from a Lecture Video Archive.Proceedings of the 2015 ACM Annual Conference on SIGUCCS. pp. 3-9. ACM (2015).
Lecture video archives offer a large variety of lecture recordings in different topics. Naturally, topics are described superficially, easily or detailed in different lectures. Users interested in certain topics have problems finding lectures describing a topic chronology from basic lectures to more detailed difficult lectures. The Lecture Butler is going to automatically offer e-learning students lectures for the topics of interest in chronological playlists. The approach is finding lecture information using title, description, OCR and ASR data. This data is indexed and searched by an in-memory database to fulfill the speed requirements for playlist creation. In the search results lectures are going to be ordered by lecture occurrence in the university semester time schedule or by given lecture level of difficulty. As a result students can automatically create playlists for their topic of interest in sequence of the lecture level. Hence, students are not overstrained by lectures when they start with basic lectures first. Basic lectures provide information to understand more complex lectures. The research shows that an automatic approach by adding the level of difficulty or university semester time table is going to show reasonable playlists to find topics of interest. This solves the main problem students encounter when they try to learn a topic step-by-step using recorded lectures. The approach will support and motivate students using e-learning opportunities.
Further Information
AbstractLecture video archives offer a large variety of lecture recordings in different topics. Naturally, topics are described superficially, easily or detailed in different lectures. Users interested in certain topics have problems finding lectures describing a topic chronology from basic lectures to more detailed difficult lectures. The Lecture Butler is going to automatically offer e-learning students lectures for the topics of interest in chronological playlists. The approach is finding lecture information using title, description, OCR and ASR data. This data is indexed and searched by an in-memory database to fulfill the speed requirements for playlist creation. In the search results lectures are going to be ordered by lecture occurrence in the university semester time schedule or by given lecture level of difficulty. As a result students can automatically create playlists for their topic of interest in sequence of the lecture level. Hence, students are not overstrained by lectures when they start with basic lectures first. Basic lectures provide information to understand more complex lectures. The research shows that an automatic approach by adding the level of difficulty or university semester time table is going to show reasonable playlists to find topics of interest. This solves the main problem students encounter when they try to learn a topic step-by-step using recorded lectures. The approach will support and motivate students using e-learning opportunities.
v. Löwis, M., Staubitz, T., Teusner, R., Renz, J., Tannert, S., Meinel, C.: Scaling Youth Development Training in IT Using an xMOOC Platform.Proceedings of the IEEE Frontiers in Education. IEEE, El Paso, TX, USA (2015).
Renz, J., Bauer, M., Malchow, M., Staubitz, T., Meinel, C.: Optimizing the Video Experience in MOOCs.Proceedings of the 7th International Conference on Education and New Learning Technologies (EduLearn). IATED, Barcelona, Spain (2015).
Che, X., Yang, H., Meinel, C.: Adaptive E-Lecture Video Outline Extraction Based on Slides Analysis.Proceedings of 2015 International Conference on Advances in Web-Based Learning (ICWL2015). pp. 59-68. , Guangzhou, China (2015).
In this paper, we propose an automated adaptive solution to generate logical, accurate and detailed tree-structure outline for video-based online lectures, by extracting the attached slides and reconstructing their content. The proposed solution begins with slide-transition detection and optical character recognition, and then proceeds by a static method of analyzing the layout of single slide and the logical relations within the slides series. Some features about the under-processing slides series, such as a �xed title position, will be �gured out and applied in the adaptive rounds to improve the outline quality. The result of our experiments shows that the general accuracy of the �nal lecture outline reaches 85%, which is about 13% higher than the static method.
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AbstractIn this paper, we propose an automated adaptive solution to generate logical, accurate and detailed tree-structure outline for video-based online lectures, by extracting the attached slides and reconstructing their content. The proposed solution begins with slide-transition detection and optical character recognition, and then proceeds by a static method of analyzing the layout of single slide and the logical relations within the slides series. Some features about the under-processing slides series, such as a �xed title position, will be �gured out and applied in the adaptive rounds to improve the outline quality. The result of our experiments shows that the general accuracy of the �nal lecture outline reaches 85%, which is about 13% higher than the static method.
v. Löwis, M., Staubitz, T., Teusner, R., Renz, J., Tannert, S., Meinel, C.: Scaling Youth Development Training in IT Using an xMOOC Platform.Proceedings of the IEEE Frontiers in Education. IEEE, El Paso, TX, USA (2015).
Luo, S., Yang, H., Meinel, C.: Reward-based Intermittent Reinforcement in Gamification for E-learning.Proceedings of the 7th International Conference on Computer Supported Education. pp. 177-184. , Lisbon, Portugal (2015).
Nowadays gamification is a hot topic in the world, a lot of websites, applications and researches adapt this method to arouse users' motivation. From the past experience, gamification indeed has a positive influence on users' motivation especially in e-learning field. However, the gamification method either is hard to be applied to professional content called meaningful gamification or is negative on user's intrinsic motivation called reward-based gamification. So we study the game addiction mechanism and propose the reward-based intermittent reinforcement method in gamification to take advantage of user independence feature in the latter one and eliminate the negative influence on user's intrinsic motivation. In order to investigate the practicability and integrate effectiveness, we implement this model in our tele-teaching platform.
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AbstractNowadays gamification is a hot topic in the world, a lot of websites, applications and researches adapt this method to arouse users' motivation. From the past experience, gamification indeed has a positive influence on users' motivation especially in e-learning field. However, the gamification method either is hard to be applied to professional content called meaningful gamification or is negative on user's intrinsic motivation called reward-based gamification. So we study the game addiction mechanism and propose the reward-based intermittent reinforcement method in gamification to take advantage of user independence feature in the latter one and eliminate the negative influence on user's intrinsic motivation. In order to investigate the practicability and integrate effectiveness, we implement this model in our tele-teaching platform.
Staubitz, T., Pfeiffer, T., Renz, J., Willems, C., Meinel, C.: Collaborative Learning in a MOOC Environment.Proceedings of the 8th annual International Conference of Education, Research and Innovation. pp. 8237-8246. IATED, Seville, Spain (2015).
Renz, J., Staubitz, T., Meinel, C.: MOOC to Go.Proc. 10th International Conference on Mobile Learning (ML 2014). , Madrid, Spain (2014).
Staubitz, T., Woinar, S., Renz, J., Meinel, C.: Towards Social Gamification - Implementing a Social Graph in an xMOOC Platform.Proc. 7th International Conference of Education, Research and Innovation. , Sevilla, Spain (2014).
Willems, C., Renz, J., Staubitz, T., Meinel, C.: Reflections on Enrollment Numbers and Success Rates at the openHPI MOOC Platform.Proceedings of the Second MOOC European Stakeholders Summit (EMOOCs2014). , Lausanne, Switzerland (2014).
Malchow, M., Bauer, M., Meinel, C.: The Future of Teleteaching in MOOC Times.2014 IEEE 17th International Conference on Computational Science and Engineering (CSE). pp. 438-443. IEEE (2014).
In the last decades, a lot of different e-learning platforms have established. There are several types of them for example teleteaching platforms. For a couple of years, MOOC platforms have come up and have been enjoying great popularity. In this paper we analyze how important teleteaching platforms are in times of MOOCs. A teleteaching platform is understood as an online service which offers live or recorded lectures as video streams. Furthermore, different concepts how teleteaching can be integrated into MOOC courses are discussed as well as approaches to analyze differences in learning outcome and behavior of students using MOOCs and teleteaching platforms. We analyze if there are urgent factors for the use of teleteaching systems with a view on students' behavior and learn success. It is further discussed how intelligent integration methods can be used to offer students an enhanced learning experience.
Further Information
AbstractIn the last decades, a lot of different e-learning platforms have established. There are several types of them for example teleteaching platforms. For a couple of years, MOOC platforms have come up and have been enjoying great popularity. In this paper we analyze how important teleteaching platforms are in times of MOOCs. A teleteaching platform is understood as an online service which offers live or recorded lectures as video streams. Furthermore, different concepts how teleteaching can be integrated into MOOC courses are discussed as well as approaches to analyze differences in learning outcome and behavior of students using MOOCs and teleteaching platforms. We analyze if there are urgent factors for the use of teleteaching systems with a view on students' behavior and learn success. It is further discussed how intelligent integration methods can be used to offer students an enhanced learning experience.
Chujfi, S., Meinel, C.: Modeling cognitive style patterns to explore individuals’ capabilities for processing knowledge in virtual settings.Proceedings of the 2014 European Conference on Cognitive Ergonomics (ECCE 2014). ACM (2014).
Organizations continue building virtual working teams (Teleworkers) to become more dynamic as part of their strategic innovation with great benefits to individuals, business, and society. Geographically distributed organizations however have the big challenge of managing people’s knowledge not only to keep operations running but also to promote innovation within the organization creating new knowledge. This study analyses how knowledge-based organizations working with decentralized staff may need considering cognitive styles (CS) and learning styles (LS) of individuals participating on their programs to effectively manage knowledge in virtual settings. The study aims at modeling patterns to identify abilities of individuals according to their cognitive and learning styles attempting to match affinities to work remotely and take part in virtual team work, and also to correctly determine the use of appropriate hypermedia tools to help overcoming lower performance and effectiveness, which may occur due to the lack face-to-face communication normally found in typical offices.
Further Information
AbstractOrganizations continue building virtual working teams (Teleworkers) to become more dynamic as part of their strategic innovation with great benefits to individuals, business, and society. Geographically distributed organizations however have the big challenge of managing people’s knowledge not only to keep operations running but also to promote innovation within the organization creating new knowledge. This study analyses how knowledge-based organizations working with decentralized staff may need considering cognitive styles (CS) and learning styles (LS) of individuals participating on their programs to effectively manage knowledge in virtual settings. The study aims at modeling patterns to identify abilities of individuals according to their cognitive and learning styles attempting to match affinities to work remotely and take part in virtual team work, and also to correctly determine the use of appropriate hypermedia tools to help overcoming lower performance and effectiveness, which may occur due to the lack face-to-face communication normally found in typical offices.
Willems, C., Fricke, N., Meyer, S., Meissner, R., Rollmann, K.-A., Voelcker, S., Woinar, S., Meinel, C.: Motivating the Masses – Gamified Massive Open Online Courses on openHPI.Proc. 6th International Conference on Education and New Learning Technologies (EDULEARN14). , Barcelona, Spain (2014).
Staubitz, T., Renz, J., Willems, C., Jasper, J., Meinel, C.: Lightweight Ad Hoc Assessment of Practical Programming Skills at Scale.Proceedings of IEEE Global Engineering Education Conference (EDUCON2014). IEEE, Istanbul, Turkey (2014).
Renz, J., Staubitz, T., Pollack, J., Meinel, C.: Improving the Onboarding User Experience in MOOCs.Proc. 6th International Conference on Education and New Learning Technologies (EDULEARN2014). , Barcelona, Spain (2014).
Staubitz, T., Renz, J., Willems, C., Jasper, J., Meinel, C.: Lightweight Ad Hoc Assessment of Practical Programming Skills at Scale.Proceedings of IEEE Global Engineering Education Conference (EDUCON2014). IEEE, Istanbul, Turkey (2014).
Renz, J., Staubitz, T., Willems, C., Klement, H., Meinel, C.: Handling Re-grading of Automatically Graded Assignments in MOOCs.Proceedings of IEEE Global Engineering Education Conference (EDUCON2014). IEEE, Istanbul, Turkey (2014).
Staubitz, T., Renz, J., Willems, C., Meinel, C.: Supporting Social Interaction and Collaboration on an xMOOC Platform.Proc. 6th International Conference on Education and New Learning Technologies (EDULEARN14). , Barcelona, Spain (2014).
Renz, J., Staubitz, T., Meinel, C.: MOOC to Go.Proc. 10th International Conference on Mobile Learning (ML 2014). , Madrid, Spain (2014).
Bauer, M., Meinel, C.: A Concept to Analyze User Navigation Behavior Inside a Recorded Lecture (to Identify Difficult Spots).Information Technology Based Higher Education and Training (ITHET). pp. 1-4. IEEE (2014).
This paper provides a brief report of our concept to scan the streaming server's log files in order to identify specific behavior of the users. A distinct form of behavior is the jump-back. Students do it when they watched a scene of a recorded lecture and then watch it again after a short amount of time. So, it can be assumed that this scene is of higher interest because it is either very interesting or hard to understand for the viewer. The knowledge of these found hotspots could be used in order to improve teaching materials such as slides and teaching style. In this paper, we describe how we plan to gather the data, how to analyze it and how the insights can be utilized. It is not only focused on the technological perspective of video-based e-learning but also on the pedagogical view.
Further Information
AbstractThis paper provides a brief report of our concept to scan the streaming server's log files in order to identify specific behavior of the users. A distinct form of behavior is the jump-back. Students do it when they watched a scene of a recorded lecture and then watch it again after a short amount of time. So, it can be assumed that this scene is of higher interest because it is either very interesting or hard to understand for the viewer. The knowledge of these found hotspots could be used in order to improve teaching materials such as slides and teaching style. In this paper, we describe how we plan to gather the data, how to analyze it and how the insights can be utilized. It is not only focused on the technological perspective of video-based e-learning but also on the pedagogical view.
Renz, J., Staubitz, T., Pollack, J., Meinel, C.: Improving the Onboarding User Experience in MOOCs.Proc. 6th International Conference on Education and New Learning Technologies (EDULEARN2014). , Barcelona, Spain (2014).
Renz, J., Staubitz, T., Willems, C., Klement, H., Meinel, C.: Handling Re-grading of Automatically Graded Assignments in MOOCs.Proceedings of IEEE Global Engineering Education Conference (EDUCON2014). IEEE, Istanbul, Turkey (2014).
Staubitz, T., Renz, J., Willems, C., Meinel, C.: Supporting Social Interaction and Collaboration on an xMOOC Platform.Proc. 6th International Conference on Education and New Learning Technologies (EDULEARN14). , Barcelona, Spain (2014).
Staubitz, T., Woinar, S., Renz, J., Meinel, C.: Towards Social Gamification - Implementing a Social Graph in an xMOOC Platform.Proc. 7th International Conference of Education, Research and Innovation. , Sevilla, Spain (2014).
Grünewald, F., Yang, H., Mazandarani, E., Bauer, M., Meinel, C.: Next Generation Tele-Teaching: Latest Recording Technology, User Engagement and Automatic Metadata Retrieval.Human Factors in Computing and Informatics. pp. 391-408 (2013).
Yang, H., Grünewald, F., Bauer, M., Meinel, C.: Lecture Video Browsing Using Multimodal Information Resources.12th International Conference on Web-based Learning (ICWL). pp. 204-213. Springer Berlin Heidelberg (2013).
Yang, H., Grünewald, F., Bauer, M., Meinel, C.: Lecture Video Browsing Using Multimodal Information Resources.12th International Conference on Web-based Learning (ICWL). pp. 204-213. Springer Berlin Heidelberg (2013).
Grünewald, F., Mazandarani, E., Meinel, C., Teusner, R., Totschnig, M., Willems, C.: openHPI - a Case-Study on the Emergence of two Learning Communities.Proceedings of 2013 IEEE Global Engineering Education Conference (EDUCON). IEEE Press (2013).
Recently a new format of online education has emerged that combines video lectures, interactive quizzes and social learning into an event that aspires to attract a massive number of participants. This format, referred to as Massive Open Online Course (MOOC), has garnered considerable public attention, and has been invested with great hopes (and fears) of transforming higher education by opening up the walls of closed institutions to a world-wide audience. In this paper, we present two MOOCs that were hosted at the same platform, and have implemented the same learning design. Due to their difference in language, topic domain and difficulty, the communities that they brought into existence were very different. We start by describing the MOOC format in more detail, and the distinguishing features of openHPI. We then discuss the literature on communities of practice and cultures of participation. After some statistical data about the first openHPI course, we present our qualitative observations about both courses, and conclude by giving an outlook on an ongoing comparative analysis of the two courses.
Further Information
AbstractRecently a new format of online education has emerged that combines video lectures, interactive quizzes and social learning into an event that aspires to attract a massive number of participants. This format, referred to as Massive Open Online Course (MOOC), has garnered considerable public attention, and has been invested with great hopes (and fears) of transforming higher education by opening up the walls of closed institutions to a world-wide audience. In this paper, we present two MOOCs that were hosted at the same platform, and have implemented the same learning design. Due to their difference in language, topic domain and difficulty, the communities that they brought into existence were very different. We start by describing the MOOC format in more detail, and the distinguishing features of openHPI. We then discuss the literature on communities of practice and cultures of participation. After some statistical data about the first openHPI course, we present our qualitative observations about both courses, and conclude by giving an outlook on an ongoing comparative analysis of the two courses.
Che, X., Yang, H., Meinel, C.: Tree-structure Outline Generation for Lecture Videos with Synchronized Slides.Proceedings of the Second International Conference on e-Learning and e-Technologies in Education (ICEEE2013). pp. 87-92. , Lodz, Poland (2013).
In this paper we propose a solution to generate tree-structure outline for lecture videos by analyzing their synchronized slides, by which detailed lecture overview can be automatically provided to E-learning portal users. Starting with OCR (Optical Character Recognition) result, we reconstruct the content of each slide. After that, we explore logical relations between slides, in order to make them hierarchical. And all potential redundant content will also be removed. Our evaluation shows that, based on our test dataset, the final outline achieved retains about 1/4 of the original texts from all slides and is organized well in an up-to-6-level tree structure. Furthermore, the average accuracy of all slide titles, which are undoubtedly the most important information, reaches 86%.
Further Information
AbstractIn this paper we propose a solution to generate tree-structure outline for lecture videos by analyzing their synchronized slides, by which detailed lecture overview can be automatically provided to E-learning portal users. Starting with OCR (Optical Character Recognition) result, we reconstruct the content of each slide. After that, we explore logical relations between slides, in order to make them hierarchical. And all potential redundant content will also be removed. Our evaluation shows that, based on our test dataset, the final outline achieved retains about 1/4 of the original texts from all slides and is organized well in an up-to-6-level tree structure. Furthermore, the average accuracy of all slide titles, which are undoubtedly the most important information, reaches 86%.
Che, X., Yang, H., Meinel, C.: Lecture Video Segmentation by Automatically Analyzing the Synchronized Slides.Proceedings of the 21st ACM international conference on Multimedia. pp. 345-348. , Barcelona, Spain (2013).
In this paper we propose a solution which segments lecture video by analyzing its supplementary synchronized slides. The slides content derives automatically from OCR (Optical Character Recognition) process with an approximate accuracy of 90%. Then we partition the slides into different subtopics by examining their logical relevance. Since the slides are synchronized with the video stream, the subtopics of the slides indicate exactly the segments of the video. Our evaluation reveals that the average length of segments for each lecture is ranged from 5 to 15 minutes, and 45% segments achieved from test datasets are logically reasonable.
Further Information
AbstractIn this paper we propose a solution which segments lecture video by analyzing its supplementary synchronized slides. The slides content derives automatically from OCR (Optical Character Recognition) process with an approximate accuracy of 90%. Then we partition the slides into different subtopics by examining their logical relevance. Since the slides are synchronized with the video stream, the subtopics of the slides indicate exactly the segments of the video. Our evaluation reveals that the average length of segments for each lecture is ranged from 5 to 15 minutes, and 45% segments achieved from test datasets are logically reasonable.
Totschnig, M., Willems, C., Meinel, C.: openHPI: Evolution of a MOOC Platform from LMS to SOA.Proceedings of the 5th International Conference on Computer Supported Education (CSEDU2013). pp. 49-54. SciTePress, Aachen, Germany (2013).
Grünewald, F., Meinel, C., Totschnig, M., Willems, C.: Designing MOOCs for the Support of Multiple Learning Styles.Proceedings of the 8th European Conference on Technology Enhanced Learning (EC-TEL 2013). Springer, Paphos, Cyprus (2013).
"Internetworking with TCP/IP" is a Massive Open Online Course, held in German at openHPI end of 2012, that attracted a large audience that has not been in contact with higher education before. The course followed the xMOOC model based on a well-defined sequence of learning content, mainly video lectures and interactive self-tests, and with heavy reliance on social collaboration features. From 2726 active participants, 38% have participated in a survey at the end of the course. This paper presents an analysis of the survey responses with respect to the following questions: 1) How can a MOOC accommodate different learning styles and 2) What recommendations for the design and organization of a MOOC can be concluded from the responses? We finally give an outlook on challenges for the further development of openHPI. Those challenges are based on didactical and technical affordances for a better support of the different learning styles. We propose an evolution of the xMOOC, that bridges the gap to the cMOOC model by developing tools that allow users to create diverging paths through the learning material, involve the user personally in the problem domain with (group) hands-on exercises and reward user contributions by means of gamification.
Further Information
Abstract"Internetworking with TCP/IP" is a Massive Open Online Course, held in German at openHPI end of 2012, that attracted a large audience that has not been in contact with higher education before. The course followed the xMOOC model based on a well-defined sequence of learning content, mainly video lectures and interactive self-tests, and with heavy reliance on social collaboration features. From 2726 active participants, 38% have participated in a survey at the end of the course. This paper presents an analysis of the survey responses with respect to the following questions: 1) How can a MOOC accommodate different learning styles and 2) What recommendations for the design and organization of a MOOC can be concluded from the responses? We finally give an outlook on challenges for the further development of openHPI. Those challenges are based on didactical and technical affordances for a better support of the different learning styles. We propose an evolution of the xMOOC, that bridges the gap to the cMOOC model by developing tools that allow users to create diverging paths through the learning material, involve the user personally in the problem domain with (group) hands-on exercises and reward user contributions by means of gamification.
Willems, C., Jasper, J., Meinel, C.: Introducing Hands-On Experience to a Massive Open Online Course on openHPI.Proceedings of IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE 2013). IEEE Press, Kuta, Bali, Indonesia (2013).
Massive Open Online Courses (MOOCs) have become the trending topic in e-learning. Many institutions started to offer courses, either on commercial platforms like Coursera and Udacity or using own platform software. While many courses share the concept of lecture videos combined with automatically assessable assignments, and discussion forums, only few courses provide hands-on experience. The design of practical exercises poses a great challenge to a teaching team and gets even more challenging if these assignments should be gradable. In the course Internetworking with TCP/IP on the German MOOC platform openHPI, the teaching team conducted an experiment with three practical tasks that were implemented as assessed bonus exercises. The exercise design was limited by the constraint that the platform software could not be adapted for these exercises and that there could be no central training environment to perform these assignments. This paper describes the experiment setup, the challenges and pitfalls and evaluates the result based on statistical data and a survey taken by the course participants.
Further Information
AbstractMassive Open Online Courses (MOOCs) have become the trending topic in e-learning. Many institutions started to offer courses, either on commercial platforms like Coursera and Udacity or using own platform software. While many courses share the concept of lecture videos combined with automatically assessable assignments, and discussion forums, only few courses provide hands-on experience. The design of practical exercises poses a great challenge to a teaching team and gets even more challenging if these assignments should be gradable. In the course Internetworking with TCP/IP on the German MOOC platform openHPI, the teaching team conducted an experiment with three practical tasks that were implemented as assessed bonus exercises. The exercise design was limited by the constraint that the platform software could not be adapted for these exercises and that there could be no central training environment to perform these assignments. This paper describes the experiment setup, the challenges and pitfalls and evaluates the result based on statistical data and a survey taken by the course participants.
Grünewald, F., Mazandarani, E., Meinel, C., Teusner, R., Totschnig, M., Willems, C.: openHPI: Soziales und Praktisches Lernen im Kontext eines MOOC.Proceedings of DeLFI 2013 - 11. e-Learning Fachtagung Informatik. Gesellschaft für Informatik, Bremen, Germany (2013).
Mit dem Format des "Masssive Open Online Courses" (MOOC) hat sich in den letzten Jahren eine intensiv diskutierte neue Variante des E-Learnings herausgebildet. Zentrale Voraussetzung eines MOOCs ist die Öffnung eines Kurses aus den Schranken einer Bildungsinstitution heraus hin zur kostenlosen und uneingeschränkten Teilnahme. Über die für ein MOOC angemessenen pädagogischen Methoden wird zur Zeit zwar heftig diskutiert, Konsens besteht allerdings darüber, dass die wichtigste Herausforderung darin besteht, Lernprozesse durch soziale Interaktionen zwischen potentiell Tausenden von Teilnehmern zu stimulieren. In unserem Beitrag stellen wir openHPI vor, eine Plattform für MOOCs im Bereich der Informationstechnologie. Anhand von Kolbs Theorie der Lernstile analysieren wir eine Umfrage unter den Teilnehmern des ersten deutschsprachigen Kurses "Internetworking", und zeigen, dass ein vorrangig am Format der Vorlesung orientiertes MOOC zwar eher dem an Begriffsbildung und Beobachtung orientierten assimilierenden Lernstil entgegenkommt, dass es uns durch die Einführung von praktischen Zusatzaufgaben jedoch auch gelang, das aktive Experimentieren der Teilnehmer mit der Materie zu fördern. Wir beschreiben auch, in welchem Ausmaß die Teilnehmer, Funktionen der Plattform, die das soziale Lernen ermöglichen, nutzen, und welche zusätzlichen Funktionen nachgefragt werden. Für die Neuauflage dieses Kurses, sowie des ersten, englischsprachigen Kurses zum Thema "In-Memory Databases" ıst eine intensivere Integration praktischer Aufgaben in das Kurs-Design geplant. Die didaktischen und technischen Herausforderungen, die sich daraus ergeben, werden im zweiten Teil des Textes analysiert.
Further Information
AbstractMit dem Format des "Masssive Open Online Courses" (MOOC) hat sich in den letzten Jahren eine intensiv diskutierte neue Variante des E-Learnings herausgebildet. Zentrale Voraussetzung eines MOOCs ist die Öffnung eines Kurses aus den Schranken einer Bildungsinstitution heraus hin zur kostenlosen und uneingeschränkten Teilnahme. Über die für ein MOOC angemessenen pädagogischen Methoden wird zur Zeit zwar heftig diskutiert, Konsens besteht allerdings darüber, dass die wichtigste Herausforderung darin besteht, Lernprozesse durch soziale Interaktionen zwischen potentiell Tausenden von Teilnehmern zu stimulieren. In unserem Beitrag stellen wir openHPI vor, eine Plattform für MOOCs im Bereich der Informationstechnologie. Anhand von Kolbs Theorie der Lernstile analysieren wir eine Umfrage unter den Teilnehmern des ersten deutschsprachigen Kurses "Internetworking", und zeigen, dass ein vorrangig am Format der Vorlesung orientiertes MOOC zwar eher dem an Begriffsbildung und Beobachtung orientierten assimilierenden Lernstil entgegenkommt, dass es uns durch die Einführung von praktischen Zusatzaufgaben jedoch auch gelang, das aktive Experimentieren der Teilnehmer mit der Materie zu fördern. Wir beschreiben auch, in welchem Ausmaß die Teilnehmer, Funktionen der Plattform, die das soziale Lernen ermöglichen, nutzen, und welche zusätzlichen Funktionen nachgefragt werden. Für die Neuauflage dieses Kurses, sowie des ersten, englischsprachigen Kurses zum Thema "In-Memory Databases" ıst eine intensivere Integration praktischer Aufgaben in das Kurs-Design geplant. Die didaktischen und technischen Herausforderungen, die sich daraus ergeben, werden im zweiten Teil des Textes analysiert.
Linckels, S., Dording, C., Kreis, Y., Reuter, R., Weber, C., Meinel, C.: Teaching with Information and Communication Technologies: Preliminary Results of a Large Scale Survey.Proceedings of the 37th annual ACM SIGUCCS fall conference (SIGUCCS fall 2009). pp. 157-162. ACM Press, St Louis, MO, USA (2009).
Repp, S., Meinel, C.: Learning through Software Development Projects in Heterogenous Groups.Proceedings of the 20th EAEEIE Annual Conference on Innovation in Education for Electrical and Information Engineering (EAEEIE 2009). , Valencia, Spain (2009).
Wang, L., Meinel, C.: X-tracking the Changes of Web Navigation Patterns.Proceedings of the 13th International Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2009). pp. 772-779. Springer, Bangkok, Thailand (2009).
Yeung, C.-M.A., Noll, M.G., Gibbins, N., Meinel, C., Shadbolt, N.: On Measuring Expertise in Collaborative Tagging Systems.Proceedings of the 1st International Conference on Society On-Line (WebSci 2009). , Athens, Greece (2009).
Wang, L., Bross, J., Meinel, C.: Post Recommendation in Political Social Web Site.Proceedings of the 8th International Conference on Electronic Government (EGOV 2009). pp. 210-221. Springer, Linz Austria (2009).
Baumann, B., Sack, H., Groß, A., Meinel, C.: Linking the tele-TASK Video Portal to the Semantic Web.Proceedings of the 9th International Conference on Innovative Internet Community Systems (I2CS 2009). pp. 205-216. GI LNI Press, Jena, Germany (2009).
Noll, M.G., Yeung, C.-M.A., Gibbins, N., Meinel, C., Shadbolt, N.: Telling Experts from Spammers: Expertise Ranking in Folksonomies.Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2009). pp. 612-619. ACM Press, Boston, USA (2009).
Repp, S., Meinel, C.: Automatic Extraction of Semantic Descriptions from the Lecturer's Speech.Proceedings of the 2009 IEEE International Conference on Semantic Computing (ICSC 2009). pp. 513-520. IEEE Press, Berkeley, CA, USA (2009).
Noll, M.G., Meinel, C.: The Metadata Triumvirate: Social Annotations, Anchor Texts and Search Queries.Proceedings of the 7th IEEE/WIC/ACM International Conference on Web Intelligence (WI 2008). pp. 640-647. IEEE CS Press, Sydney, Australia (2008).
Repp, S., Groß, A., Meinel, C.: Webbasierte Suche in Vorlesungsvideos auf Basis der Transkripte eines Spracherkenners.Proceedings of the 6th International Conference on e-Learning Fachtagung Informatik P-132 (DeLFI 2008). pp. 427 - 428. Gesellschaft für Informatik, Bonn, Lübeck, Germany (2008).
Repp, S., Linckels, S., Meinel, C.: Question Answering from Lecture Videos based on an Automatic Semantic Annotation.Proceedings of the 13th annual conference on Innovation and technology in computer science education (ITiCSE 2008). pp. 17 - 21. ACM Press, Madrid, Spain (2008).
Cordel, D., Meinel, C., Repp, S., Willems, C.: Explorative Learning of Wireless Network Security with Tele-Lab IT-Security.Proceedings of the 1st international conference on Hybrid Learning and Education (ICHL 2008). pp. 213 - 224. Springer, Hong Kong, China (2008).
Noll, M.G., Meinel, C.: Building a Scalable Collaborative Web Filter with Free and Open Source Software.Proceedings of the 4th IEEE International Conference on Signal-Image Technology & Internet-based Systems (SITIS 2008). pp. 563-571. IEEE CS Press, Bali, Indonesia (2008).
Repp, S., Meinel, C.: Segmentation of Lecture Videos based on Spontaneous Speech Recognition.Proceedings of the 10th IEEE Workshop International Symposium on Multimedia (ISMW 2008). pp. 692-697. IEEE Press, Berkeley, California, USA (2008).
Linckels, S., Meinel, C.: Applications of Description Logics to Improve Multimedia Information Retrieval for Efficient Educational Tools.Proceeding of the 1st ACM international conference on Multimedia information retrieval (MIR 2008). pp. 321 - 328. ACM Press, Vancouver, Canada (2008).
Repp, S., Linckels, S., Meinel, C.: Automatische Erzeugung Semantischer Annotationen für Vorlesungsvideos.Proceedings of the 6th International Conference on e-Learning Fachtagung Informatik P-132 (DeLFI 2008). pp. 149-160. Gesellschaft für Informatik, Bonn, Lübeck, Germany (2008).
Repp, S., Groß, A., Meinel, C.: Dynamic Browsing of Audiovisual Lecture Recordings based on Automated Speech Recognition.Proceedings of the 9th international conference on Intelligent Tutoring Systems (ITS 2008). pp. 662 - 664. Springer, Montreal, Canada (2008).
Repp, S., Meinel, C., Yakhyayeva, S.: Motivation of the Students in Game Development Projects.Proceedings of the 13th annual conference on Innovation and technology in computer science education (ITiCSE 2008). pp. 368 - 368. ACM Press, Madrid, Spain (2008).
Karam, N., Linckels, S., Meinel, C.: Semantic Composition of Lecture Subparts for a Personalized e-Learning.Proceedings of the 4th International Conference on the Semantic Web: Research and Applications (ESWC 2007). pp. 716 - 728. Springer, Innsbruck, Austria (2007).
Repp, S., Ziegler, R., Meinel, C.: Lernortkooperation in der IT-Ausbildung - Kompetenzentwicklung in Projekten.Proceedings of the 12th GI-Fachtagung Informatik und Schule Infos (INFOS 2007). pp. 135- 146. GI Press, Siegen, Germany (2007).
Wolf, K., Linckels, S., Meinel, C.: Teleteaching Anywhere Solution Kit (tele-TASK) Goes Mobile.Proceedings of the 35th annual Association of Computing Machinery (ACM) Special Interest Group on University and College Computing Services (SIGUCCS) 2007 Fall Conference (SIGUCCS 2007). pp. 366 - 371. ACM Press, Orlando, Florida, USA (2007).
Repp, S., Waitelonis, J., Sack, H., Meinel, C.: Segmentation and Annotation of Audiovisual Recordings based on Automated Speech Recognition.Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007). pp. 620 - 629. Springer, Birmingham, UK (2007).
Wang, L., Meinel, C.: Detecting the Changes of Web Students' Learning Interest.Proceedings of the 6th International Conference on Web Intelligence (WI 2007). pp. 816 - 819. IEEE Press, Silicon Valley, USA (2007).
Wang, L., Meinel, C.: Mining the Students' Learning Interest in Browsing Web-Streaming Lectures.Proceedings of IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007). pp. 194 - 201. IEEE Press, Honolulu, USA (2007).
Repp, S., Linckels, S., Meinel, C.: Towards to an Automatic Semantic Annotation for Multimedia Learning Objects.Proceedings of the 1st international workshop on Educational multimedia and multimedia education (EMME 2007). pp. 19 - 26. ACM Press, Augsburg, Germany (2007).
Linckels, S., Dording, C., Meinel, C.: Bessere Schulnoten mit MatES, dem e-Bibliothekardienst für den Mathematikunterricht.Proceedings of the 4th Deutschen e-Learning Fachtagung der Gesellschaft für Informatik (DeLFI 2007). pp. 91 - 102. GI Press, Siegen, Germany (2007).
Noll, M.G., Meinel, C.: Personalization 2.0: Web Search Personlization via Social Bookmarking and Tagging.Proceedings of the 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference (ISWC 2007 + ASWC 2007). pp. 367 - 380. Springer, Busan, South Korea (2007).
Linckels, S., Repp, S., Karam, N., Meinel, C.: The Virtual Tele-TASK Professor---Semantic Search in Recorded Lectures.Proceedings of the 38th technical symposium on Computer science education (SIGCSE 2007). pp. 50 - 54. ACM Press, Covington, Kentucky, USA (2007).
Wolf, K., Meinel, C.: Tele-TASK - Methoden des Teleteachings.Proceedings of Hans-Joachim Laabs (Ed.): MultimeDies 2007. Wir gehen multimedial. Kommt Ihr mit? (MultimeDies 2007). pp. 50-55. , Potsdam, Germany (2007).
Linckels, S., Sack, H., Meinel, C.: Optimizing the Retrieval of Pertinent Answers for NL Questions with the E-Librarian Service.Proceedings of the 1st Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web (SMR2 2007). Springer, Busan, South Korea (2007).
Linckels, S., Sack, H., Meinel, C.: Retrieval Optimization of Pertinent Answers for NL Questions with the E-Librarian Service.Proceedings of the 6th International Semantic Web Conference (ISWC 2007) (2007).
