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Organisation

Humboldt-Universität zu Berlin Department of Informatics Computer Science Education / Computer Science and Society Seminar „Educational Data Mining“. Organisation. Place: RUD26, R. 1.307 Date: Thursdays , 9:15 – 10:45 Uhr Seminar assessment criteria: 30min. talk + 15min. Discussion

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Organisation

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  1. Humboldt-Universität zu BerlinDepartment of InformaticsComputer Science Education / Computer Science and SocietySeminar „Educational Data Mining“

  2. Organisation • Place: RUD26, R. 1.307 • Date: Thursdays, 9:15 – 10:45 Uhr • Seminar assessment criteria: • 30min. talk + 15min. Discussion • Slides giving 1 week before the talk via email (if you like) • Attendance at classmates' presentations • Active participation in discussions • Paper (approx. 10 pages including references) • Website/additional information: • http://cses.informatik.hu-berlin.de/ • For Students -> Teaching -> SS2015 -> Educational Data Mining • See also: topics for thesis in the field of intelligent learning systemshttps://cses.informatik.hu-berlin.de/for_students/thesis.php

  3. Seminar dates – schedule (1) KW16 (16.04.): Introduction, presentation of topics KW17 (23.04.): free (final assignment of topics) KW18 (30.04.): free KW19 (07.05.): free KW20 (14.05.): free KW21 (21.05.): free • KW22 (28.05.): Talk 1 and 2

  4. Seminar dates – schedule (2) KW23 (04.06.): Talk 3 and 4 KW24 (11.06.): Talk 5 and 6 KW25 (18.06.): Talk 7 and 8 • KW26 (25.06.): free KW27 (02.07.): Buffer … KW40 (30.09.): Paper Due

  5. Introduction to EDM • What is Educational Data Mining (EDM)?

  6. Methods in EDM

  7. Methods in EDM

  8. Who & What can EDM help? • Students/learners • Hint generation (Barnes, T. et.al 2008) • Personalized courseware recommendation (Chen, C. et al. 2004) • Recommend learning partners (Huang, Jeff JS et al. 2010) • …

  9. Who & What can EDM help? • Teachers/instructors • Detect gaming system (Ryan Baker) • Predict motivation level (Mihaela Cocea et al. 2006) • Assess learners’ performance (Chih-Ming Chen et al. 2009) • …

  10. Who & What can EDM help? • Administrators/policy makers • The impact of curriculum revisions (Becker, K. et al. 2000) • Course Planning of extension education (Hsia, T. et al. 2008) • Select students for remedial classes (Ma, Y. et al. 2000) • …

  11. Subjects/Topics Theoretical Foundations • Math. methods and approaches • Data Mining Techniques • Artificial Intelligence Educational Data Mining in Intelligent Tutoring Systems • Overview of intelligent/adaptive learning systems • Psychological and educational foundations • Relation between EDM and ITS Learning Analytics • Methods and approaches • Technologies and applications • Future development

  12. Topics „Theoretical foundations“ (1) • Introduction to Educational Data Mining and Learning Analytics(Topic for 2 students) • What is Educational Data Mining? • What is Learning Analytics? • What are differences/similarities between both? • What are typical data mining techniques and what are their goals? • How can these techniques be used to enhance learning? • Online Discussion • How to faciliate online discussion? • What are the popular text ming techonologies? • What insights gained from the online discussion so far? • What are the future issues?

  13. Topics „Theoretical Foundations“ (2) • Clustering • What is Clustering and how does it work? • What is Spectral Clustering and what is its purpose? • What is model-based clustering? • What are drawbacks of the K-Means clustering? • Are there other clustering methods and for what can they be used? • Students‘ Engagement at MOOC • What factors would exert influence on the students‘ engagement ? • How to address the high drop-out rate problem? • Learning Behaviors Mining • How to mine learning behaviors? • What are difficulties of the current mining technoloigies? • Is it possible to predict the learning outcomes using the extracted learning behavior patterns?

  14. Topics „Theoretical Foundations“ (3) • Social Network Analysis • What are challeneges of Social Network Analysis methods in Educational Data Mining? • What are goals of Social Network Analysis and how to achieve? • Swarm Intelligence • What is Swarm Intelligence? • What are typical algorithms and applications related to learning? • What are pros/cons of Swarm Intelligence? • Analysis of Student/Student- and Student/Tutor-Interactions • How can computer-based interactions between students (and teachers) be identified? • How can such analysis lead to clearer understanding and improvements in learning?

  15. Topics „Educational Data Mining in Intelligent Tutoring Systems“ (1) • Introduction to Intelligent Tutoring Systems and Student Modeling (Topic for 3 students) • What are Intelligent Tutoring Systems and what are their purpose? • What are goals and challenges in ITSs? • What are typical components of such systems? • What means student modeling and what is its purpose? • Which theories are ITSs build on? • What are authoring tools for ITSs and how do they work? • Cognitive Tutors • What are Cognitive Tutors and how do they work? • On which theories are Cognitive Tutors based and which approach do they implement? • How can Educational Data Mining be used in Cognitive Tutors?

  16. Topics „Educational Data Mining in Intelligent Tutoring Systems“ (2) • Constraint-based Tutors • What are Constraint-based Tutors? • What are their mathematical basis? • Dialogue-based Tutors • What are Dialogue-based Tutors and how do they work? • What are fundamental principles of Natural Language Processing? • How can Educational Data Mining help improve such systems?

  17. Topics „Educational Data Mining in Intelligent Tutoring Systems“ (3) • Affective Intelligent Tutoring System • What are Affective ITS? • Which affective states exisit and how do they influence learning? • How can affective states be identified and considered in learning? • Misuse of Intelligent Tutoring Systems • What means misuse in ITSs? • How does misuse influence learning? • How can it be identified? • How can misuse be prevented?

  18. Topics „Learning Analytics“ (1) • Visualization • How can learning data be processed and visualized using Learning Analytics? • What are the benefits of such visualization? • Where and for what purpose can visualizations be used? • Recommendation • How can recommendations improve learning? • How can Learning Analytics help generate recommendations for learners/educators? • Where can recommendations be used to improve learning processes? • Assessment • How can computer-based systems be used to support the assessment of learners and their activities? • What role plays Learning Analytics in this context? • Where are assessment systems used today in real life?

  19. Topics „Learning Analytics“ (2) • Social Learning Analytics • What is Social Learning Analytics? • How can it be used? • How can it facilitate/support learning in social networks? • Massive Open Online Courses (MOOCs) • What are MOOCs and how do they work? • How can Learning Analytics improve MOOCs?

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