Artificial IntelligenceBased Student Learning Evaluation A Concept MapBased

Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student’s Understanding of a Topic Presenter: NENG-KAI, HONG Authors: G. PANKAJ JAIN, VARADRAJ P. GURUPUR, JENNIFER L. SCHROEDER, AND EILEEN D. FAULKENBERRY 2014, IEEE Intelligent Database Systems Lab

Outlines n Motivation n Objectives n Methodology n Experiments n Conclusions n Comments 1 Intelligent Database Systems Lab

Motivation • Traditional method of concept map can only be used to measure what the student knows about a subject. • Concepts developed by students should be more measurable and comparable. 2 Intelligent Database Systems Lab

Objectives • Development of a comparative analysis using probability distribution to compare concept maps developed by students. 3 Intelligent Database Systems Lab

Methodology 4 Intelligent Database Systems Lab

Methodology 5 Intelligent Database Systems Lab

Methodology 6 Intelligent Database Systems Lab

Methodology 7 Intelligent Database Systems Lab

Methodology 8 Intelligent Database Systems Lab

Methodology 9 Intelligent Database Systems Lab

Methodology 10 Intelligent Database Systems Lab

Methodology 11 Intelligent Database Systems Lab

Experiment 12 Intelligent Database Systems Lab

Experiment 13 Intelligent Database Systems Lab

Conclusions • Use of AISLE considerably reduces the time involved in assessing a student’s understanding of a topic in study for the instructor. • The method used to assess concept maps does not work very well when the concept maps submittedby the students are not hierarchical in nature 14 Intelligent Database Systems Lab

Comments • Applications – Concept maps, evalution, probability distributions 15 Intelligent Database Systems Lab