Assessment and Learning in Knowledge Spaces (ALEKS) Adaptive System Impact on Students’ Perception and Self-Regulated Learning Skills

Abstract

Adaptive learning is an educational method that uses computer algorithms and artificial intelligence (AI) to customize learning materials and activities based on each user’s model. Adaptive learning has been used for more than 20 years. However, it is still unique, and no other system could bring more or even similar capabilities than the ones adaptive technology offers, including the application of AI, psychology, psychometrics, machine learning, and providing a personalized learning environment. However, there are not many studies on its practicality, usefulness, improving students’ learning skills, students’ perception, etc., due to the limited number of institutes investing in this new technology. This paper presents the results of administering the newly developed Adaptive Self-regulated Learning Questionnaire (ASRQ) in an adaptive learning course equipped with the ALEKS (Assessment and Learning in Knowledge Spaces) system to study the amount of Self-regulated Learning Skills (SRL) score change, if any, of the students. The ASRQ was administered at the beginning and end of the semester as a pretest and posttest. Then, the quantitative Sample Paired t Test was run to measure the students’ SRL score change between the beginning and end of the semester. The results showed a significant decline in students’ SRL skills score while working with ALEKS. This paper also discusses the reasons for the considerable drop in SRL skills based on students’ perception and feedback collected through administering an open-ended survey at the end of the semester. The survey’s qualitative analysis showed various possible factors contributing to the decline of the SRL skills score, including lack of motivation, system complexity, hard penalty, lack of social presence, and lack of system practicality.