Seminar "Educational Data Mining"

Kurzbeschreibung

Das Seminar behandelt aktuelle (Forschungs-)Themen im Bereich Educational Data Mining. Ausgehend von den Grundlagen des Data Minings werden Anwendungen im Bereich von (intelligenten) Lernsystemen untersucht. Es werden u.a. Fragestellungen behandelt, die den Einsatz und den Nutzen von maschinellem Lernen in Systemen untersuchen, die das menschliche Lernen unterstützen sollen.

Ansprechpartner

Prof. Dr. Niels Pinkwart
Zhilin Zheng (organistorische Fragen und Fragen zu Themen im Bereich EDM)
Sebastian Groß (Fragen zu Themen im Bereich Intelligent Tutoring System und Learning Analytics)

Termine & Leistungsnachweis-Bedingungen, Präsentationstechnik

Regelmäßiger Termin: Donnerstag 9-11 Uhr, Rudower Chaussee 26, 1.307

Zur Erlangung eines Seminarscheines wird ein Vortrag (30 min.), die Leitung der Diskussion zum eigenen Vortrag (ca. 15 min.) und eine schriftliche Ausarbeitung (~10 Seiten) gefordert. Es wäre sinnvoll, wenn Sie einige Fragen vorbereiten wüden, die die Grundlage für die anschließende Diskussion bilden. Sie können ihre Folien (mindestens eine Woche vor dem Vortrag) per E-Mail an zhilin.zheng@informatik.hu-berlin.de senden, um Feedback zu erhalten.

Das Seminar startet am 16.04.2015 mit einer kurzen Einführung in Educational Data Mining und der Vorstellung der Themen. Anschließend können Themen ausgewählt werden. Die genauen Termine für die Vorträge werden (in Abhängigkeit von der tatsächlichen Teilnehmerzahl) zu einem späteren Zeitpunkt bekanntgegeben. Bitte melden Sie sich über GOYA an.

Vorträge und schriftliche Ausarbeitungen müssen in englischer Sprache gehalten bzw. verfasst werden. Spätester Abgabetermin für die schriftlichen Ausarbeitungen ist der 30.09.2015.

Für die Vorträge stehen ein Videobeamer (mit VGA-/HDMI-Anschluss) und eine (Kreide-)Tafel zur Verfügung. Ein tragbarer Rechner (Adobe Reader, Firefox, PowerPoint, LibreOffice) kann bei rechtzeitiger Voranmeldung zur Verfügung gestellt werden (vorheriges Testen wird empfohlen).

Die Folien des Einführungsvortrags können Sie unter folgendem Link herunterladen: Folien Einführung

Themen

Nr Thema Referenzen Vortragende(r) Vortragstermin Folien
Educational Data Mining (EDM)
1 Introduction to Educational Data Mining (EDM) and Learning Analytics (LA) [49, 50, 22, 58, 59] - -
2 Online Discussion [75, 79, 84, 81] - -
3 Clustering [63, 1, 64] David S. 28.05.2015,
9:15 - 10:00 Uhr
4 Students’ engagement at MOOC [74, 73, 78, 82, 83] David R. R. 28.05.2015,
10:00 - 10:45 Uhr
5 Social Network Analysis [44, 48, 17, 46] Björn G. 04.06.2015,
9:15 - 10:00 Uhr
6 Swarm Intelligence [29, 34, 70, 56] Alfonso V. 04.06.2015,
10:00 - 10:45 Uhr
7 Learning Behaviors Mining [76, 77, 80] - -
8 Analysis of Student/Student and Student/Tutor Interactions [6, 37, 20] - -
Intelligent Tutoring System (ITS)
9 Introduction to Intelligent Tutoring Systems (ITS) and Student Modeling [14, 15, 41, 42, 65,
10, 60, 26, 11, 4]
- -
10 Cognitive Tutors [2, 33, 12] - -
11 Constraint-Based Tutors (CBT) [38, 39, 30, 43] - -
12 Dialogue-Based Tutors [28, 27, 35, 8] - -
13 Affective ITS [36, 19, 5] - -
14 Misuse / "Gaming the system" [3, 68, 40] Leif-Nissen L. 11.06.2015,
9:15 - 10:00 Uhr
Learning Analytics (LA)
15 Visualization [53, 67, 21] - -
16 Recommendation [66, 23, 25] - -
17 Assessment [18, 55, 71, 7] - -
18 Social Learning Analytics (LSA) [57, 24, 54] - -
19 Massive Open Online Courses (MOOCs) [9, 32, 62] Tony S. 11.06.2015,
10:00 - 10:45 Uhr

Literaturverzeichnis

Nachfolgend finden Sie die Literaturreferenzen, die Ihnen einen Einstieg in ihr Thema ermöglichen und als Ausgangspunkt für eine weitere Literaturrecherche dienen sollen. Sämtliche Dokumente können Sie von uns in digitaler Form (als PDF/Word-Dokument) erhalten. Schreiben Sie dazu bitte eine E-Mail an sebastian.gross@informatik.hu-berlin.de mit Angabe der Referenznr. bzw. ihres Themas.

Darüber hinaus möchten wir Ihnen folgende Handbücher zu Educational Data Mining und Intelligent Tutoring Systems empfehlen:

Nr. Referenz
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[4] R. S. Baker, A. T. Corbett, and V. Aleven. More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In Proceedings of the 9th International Conference on Intelligent Tutoring Systems, ITS '08, pages 406-415, Berlin, Heidelberg, 2008. Springer-Verlag.
[5] R. S. J. d. Baker, S. K. D'Mello, M. T. Rodrigo, and A. C. Graesser. Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments. Int. J. Hum.-Comput. Stud., 68(4):223-241, Apr. 2010.
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