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Abstract Title: Combining expert- and cluster-analysis for Group-level personalization
Abstract: Classifying students into groups that exhibit similar behavioral patterns so they can be treated with a tailored instruction is a new research direction that attracts both  the Physics Education and the Learning Analytics research communities. Typically, personalization  approaches center on the individual learner, and identify the knowledge components required for solving each problem based on expert task analysis or educational data mining. However, individual-level personalization is less appropriate in the reality of science classes, and may also miss the advantages of group work and discussions. Moreover, previous studies have shown that experts may miss  latent knowledge structures that bottom-up analyses such as Exploratory Factor Analysis (EFA) may help to reveal.

In this study we examined how cluster analysis can reveal additional cognitive dimensions not identified by the experts. We used cluster analysis and not EFA which is typically used for this purpose  because our end goal was using the cognitive dimensions to provide the teachers with sub-groups of students sharing similar profiles.

The research tool that we used was an instrument on Magnetism. The items were analyzed by experts and the knowledge components identified for each item were represented in a Q-matrix. The instrument was administered to ~300 12th grade Physics majors. K-means cluster analysis revealed that subgroups can be differentiated by type of knowledge structures (e.g. procedural vs. declarative), resulting in improved labeling of the questions. In follow-up interviews the teachers expressed their satisfaction with the usefulness of this representation for tailoring their teaching to each group's difficulties and strengths.
Abstract Type: Contributed Poster Presentation
Session Time: Poster Session 2 Room C
Poster Number: 2C-17

Author/Organizer Information

Primary Contact: Eliran Chen
Weizmann Institute of Science
Rehovot, Non U.S.
Co-Author(s)
and Co-Presenter(s)
Tanya Nazaretsky
Giora Alexandron
Edit Yerushalmi

Weizmann Institute of Science