AI-Based Diagnostic Assessment System: Integrated With Knowledge Map in MOOCs
Chia-An Lee, Nen‐Fu Huang, Jian-Wei Tzeng, Pin-Han Tsai
Abstract
Massive open online courses offer a valuable platform for efficient and flexible learning. They can improve teaching and learning effectiveness by enabling the evaluation of learning behaviors and collection of feedback from students. The knowledge map approach constitutes a suitable tool for evaluating and presenting students' learning performance levels. This study proposes an artificial intelligence–based knowledge assessment system that integrates knowledge maps to determine students' familiarity with and mastery of course contents. This study employs a structural approach encompassing data collection, data pre-processing, model training, testing, and evaluation. In detail, the system can then customize the knowledge maps and recommend videos according to the knowledge nodes. Students consequently dedicate additional time to studying concepts with which they are unfamiliar and adjust their learning efforts accordingly. After teachers and teaching assistants have captured students' performance metrics and idiosyncratic weaknesses through knowledge maps, teachers can modify the teaching materials. Through the use of education data mining and learning analytics, our system can benefit both teachers and online learners. We hope that the proposed system provides a more personalized and intelligent online learning environment within which students can learn in a more efficient and flexible manner.