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Praxisprojekt "Design and implementation of an analytics dashboard for Moodle" - Einzelansicht

  • Funktionen:
Grunddaten
Veranstaltungsart Praxisprojekt Langtext
Veranstaltungsnummer Kurztext
Semester WiSe 2022/23 SWS 12
Erwartete Teilnehmer/-innen Max. Teilnehmer/-innen 30
Credits Belegung Keine Belegpflicht
Zeitfenster
Hyperlink
Sprache Englisch
Termine Gruppe: [unbenannt] iCalendar Export für Outlook
  Tag Zeit Rhythmus Dauer Raum Raum-
plan
Status Bemerkung fällt aus am Max. Teilnehmer/-innen E-Learning
Einzeltermine ausblenden
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Mi. 12:00 bis 14:00 wöch. LK - LK 052       Präsenzveranstaltung
Einzeltermine:
  • 12.10.2022
  • 19.10.2022
  • 26.10.2022
  • 02.11.2022
  • 09.11.2022
  • 16.11.2022
  • 23.11.2022
  • 30.11.2022
  • 07.12.2022
  • 14.12.2022
  • 21.12.2022
  • 11.01.2023
  • 18.01.2023
  • 25.01.2023
  • 01.02.2023
Gruppe [unbenannt]:
 
 


Zugeordnete Person
Zugeordnete Person Zuständigkeit
Chounta, Irene-Angelica , Prof. Dr.
Zielgruppen/Studiengänge
Zielgruppe/Studiengang Semester Pflichtkennzeichen
Master of Science Angewandte Informatik (Ingenieur- oder Medieninfor, Master of Science Angewandte Informatik (Ingenieur- oder Medieninfor 1 - 3 WP
Bachelor of Science Angewandte Informatik (Ingenieur- oder Medieninfor, Bachelor of Science Angewandte Informatik (Ingenieur- oder Medieninfor 5 - 6 WP
Master of Science Angewandte Kognitions- und Medienwissenschaft, Master of Science Angewandte Kognitions- und Medienwissenschaft 1 - 3 WP
Bachelor of Science Angewandte Kognitions- und Medienwissenschaft, Bachelor of Science Angewandte Kognitions- und Medienwissenschaft 5 - 5 WP
Zuordnung zu Einrichtungen
Informatik und Angewandte Kognitionswissenschaft
Inhalt
Bemerkung

Es stehen je 10 Plätze für BSc. und MSc. KOMEDIA, sowie je 5 Plätze für BSc. und MSc. Ang. Informatik zur Verfügung

 

Description
This project aims to develop a data dashboard to support students who struggle in online learning courses facilitated by Learning Management Systems (LMSs). To that end, we will follow a two-step approach:
a) identify potentially struggling students using the log files of a Higher Education online course (data will be provided);
b) propose automatic or semi-automatic feedback interventions to support students’ learning.

To identify struggling students, we will engineer data-based features (Jiang, et al, 2018) that may indicate - among others - lack of background knowledge, motivation, poor time-planning, and self-regulation skills. To design feedback interventions, we will use as a starting point the five levels of verbal and non-verbal interventions that tutors can employ on the learner’s progress (Wood, Wood, & Middlestone, 1978) and the concept of Contingent Tutoring (Wood, 2001).

For this project, we will work in groups of 4 students on a predefined topic (each group will be assigned a different topic). During the first weeks, we will provide an overview of the project, its goals, and related theoretical and practical concepts. Then, the groups will work towards three milestones:
a. An initial presentation of their ideas, strategies and workplans to address the project’s topic;
b. A mid-term presentation of the work-progress up to that point
c. A final presentation of the finished project and a written project report in the form of a scientific article.

Registration
You can register via Moodle https://moodle.uni-due.de/course/view.php?id=35891 using the enrolment key: DIADaM2022

Other info

• During the semester, we will offer weekly meetings and consultation sessions.
• Participation in milestone events, weekly meetings and consultation sessions is mandatory.
• This project will follow a blended-learning format (face-to-face milestone and consultation; online hackathon sprints).
• The working language for this project is English.
• Maximum number of places (students): 30, Registration mode: first come, first served ☺

Target Audience
▪ KOMEDIA Bachelor/ Master
▪ Angewandte Informatik Bachelor/ Master

Date and Location
• Wednesday, 12:00 - 14:00
• Fully Online (real-time webinars, video recordings, group consultation sessions).
• Starts on October, 12th

Prerequisites
• Basic knowledge of data analysis and programming (Python and / or R)
• Interest in data analytics and applied data science
• Imagination (wild)

Instructor
• Irene-Angelica Chounta, Prof. Dr.

References
Wood, D., Wood, H., & Middleton, D. (1978). An experimental evaluation of four face-to-face teaching strategies. International journal of behavioral development, 1(2), 131-147.
Wood, D. (2001). Scaffolding, contingent tutoring, and computer-supported learning. International Journal of Artificial Intelligence in Education, 12(3), 280-293.
Wood, H., & Wood, D. (1999). Help seeking, learning and contingent tutoring. Computers & Education, 33(2-3), 153-169.
Henderson, M., Ajjawi, R., Boud, D., & Molloy, E. (Eds.). (2019). The Impact of Feedback in Higher Education: Improving assessment outcomes for learners. Springer Nature.
Jiang, Y., Bosch, N., Baker, R. S., Paquette, L., Ocumpaugh, J., Andres, J. M. A. L., ... & Biswas, G. (2018, June). Expert feature-engineering vs. deep neural networks: Which is better for sensor-free affect detection?. In International conference on artificial intelligence in education (pp. 198-211). Springer, Cham.


Strukturbaum
Keine Einordnung ins Vorlesungsverzeichnis vorhanden. Veranstaltung ist aus dem Semester WiSe 2022/23 , Aktuelles Semester: WiSe 2023/24