Examining adaptive learning impact on students' academic performance and perception of self-regulated learning skills

Harati, Hoda (2021) Examining adaptive learning impact on students' academic performance and perception of self-regulated learning skills. Doctoral thesis, Northern Arizona University.

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Abstract

Adaptive Learning, as a new online learning environment, intends to individualize the learning experience for each individual learner. Adaptive Learning suffers many shortcomings, as the date of the publication of this dissertation, however, it is considered the next generation of the learning environment for at least the next twenty years; because of its unique features such as the application of Artificial Intelligence, knowledge state, individualized learning, real-time computerized assessment, and immediate feedback. This new complex learning environment requires self-regulated learners who act autonomously and independently; However, there appears to be no existing model to conceptualize different aspects of SRL skills in Adaptive Learning Environments (ALE’s). The purpose of this study was to empirically evaluate a theoretical model of self-regulated learning (SRL) in ALE’s. In doing so, an eight-factor Adaptive Self-Regulated Learning (ASR) Model was designed based on which a 5-point Likert scale questionnaire (ASRQ) and an open-ended survey were developed and validated to qualitatively and quantitatively incorporate the SRL skills into ALE’s. The ASRQ, then, was used to empirically evaluate the predictive function of eight SRL skills of adaptive learning students working with an adaptive learning system (ALEKS). This questionnaire, moreover, investigated the improvement of SRL skills of students during a semester. Furthermore, the open-ended survey was employed to measure students’ perceptions of learning through an adaptive learning system. One hundred twenty undergraduate students in Chemistry 151 course, equipped with ALEKS, participated and filled out the questionnaire and survey in the beginning and at the end of a semester. The result of a quantitative analysis of the questionnaire indicated three out of eight SRL variables, including Goal-setting, Time-management, and Persistence, were the predictive utilities of the final grade. Also, the result of comparing the pre with post SRL scores showed a significant drop in SRL skills of the students conveying the malfunctioning of the system to improve these skills in the corresponding students. On the other hand, the analysis of the qualitative survey indicated a varied spectrum of students’ ideas about their perception, experience, and SRL skills while working with ALEKS. Additionally, the Educational Data Mining (EDM) was utilized to create comprehensive and a big data set from ALEKS system models, students’ self-report quantitative questionnaire, and the qualitative survey. These three sources of data were combined, classified, clustered, analyzed, and compared to indicate any abnormalities or similarities in the data gathered from these three sources. The results of EDM showed a similar pattern in this big comprehensive data set indicating the failing of the system to develop the SRL skills in students and highlighting the importance of Time-management, Goal-setting, Self-evaluation, and Persistence SRL skills in the ALEKS adaptive learning system.

Item Type:	Thesis (Doctoral)
Publisher’s Statement:	© Copyright is held by the author. Digital access to this material is made possible by the Cline Library, Northern Arizona University. Further transmission, reproduction or presentation of protected items is prohibited except with permission of the author.
Keywords:	Adaptive Learning; Adaptive Learning System; Adaptive Self-Regulated Learning Model; Adaptive Self-Regulated Learning Questionnaire; Educational Data Mining; Self-Regulated Learning Skills
Subjects:	L Education > LB Theory and practice of education
NAU Depositing Author Academic Status:	Student
Department/Unit:	Graduate College > Theses and Dissertations College of Education > Teaching and Learning
Date Deposited:	01 Mar 2022 16:55
Last Modified:	01 Mar 2022 16:55
URI:	https://openknowledge.nau.edu/id/eprint/5770

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