Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017

Xie, Haoran; Chu, Hui‐Chun; Hwang, Gwo‐Jen; Wang, Chun Chieh

doi:10.1016/j.compedu.2019.103599

Cited by 321 publications

(161 citation statements)

References 33 publications

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“…The experiment conducted in this study varies in comparison to previous work on learning gains in AES in that it examines two distinct student characteristics: the information perception dimension of learning style and knowledge level. Other studies have been limited by study design and small sample size (Akbulut & Cardak, 2012;Brown et al, 2009;Xie et al, 2019). This study is unique in that it reports on the execution of a carefully designed and controlled experiment involving a reasonably large number of subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Adaptivity facilitates student learning by recommending suitable learning strategies, offering relevant instruc-tional materials, and guiding navigation through the material (Brusilovsky, 1996). Adaptive and personalized learning based on knowledge level, preferences, and learning style is always an important educational consideration (Xie et al, 2019). Multiple factors involved in developing effective elearning systems, such as student characteristics, the complexity of matching learning material and their sequences to specific characteristics, and the need for such systems to follow sound instructional models create challenges for researchers.…”

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

“…There have been many attempts to build and evaluate AESs. However, there is a shortage of studies that take into account detailed, carefully designed, and controlled experimental evaluations that assess learning effectiveness (Akbulut & Cardak, 2012;Normadhi et al, 2019;Özyurt & Özyurt, 2015;Rodrigues et al, 2019;Truong, 2016;Xie et al, 2019). Further, while there are a number of research studies based on learning style, these studies typically are short-term in nature, focus on a specific model or theory, and utilize small samples of subjects (Akbulut & Cardak, 2012;E.…”

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

“…Adaptive learning has emerged as a fundamental concept and paradigm for modern e-learning systems (Rodrigues et al, 2019), and is rising in prevalence in educational technology research (Xie, Chu, Hwang, & Wang, 2019). However, the implementation of adaptation is not always apparent in elearning systems, especially adaptation that takes into consideration both learning style and knowledge level (Brusilovsky & Millán, 2007;Truong, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…J. Brown et al, 2009;Xie et al, 2019). Consequently, more empirical research on the effect of adaptation is needed (Akbulut & Cardak, 2012;E.…”

Section: Introductionmentioning

confidence: 99%

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Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Alshammari

Qtaish

2019

JITE:Research

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Aim/Purpose: Effective e-learning systems need to incorporate student characteristics such as learning style and knowledge level in order to provide a more personalized and adaptive learning experience. However, there is a need to investigate how and when to provide adaptivity based on student characteristics, and more importantly, to evaluate its value in learning enhancement. This study aims to bridge that gap by examining the effect of different modes of learning material adaptation and their sequences to the learning style and knowledge level of students in e-learning systems. Background: E-learning systems aim to provide acceptability and interactivity between students, instructors, and learning content anytime and anywhere. However, traditional systems are typically designed for generic students irrespective of individual requirements. Successful e-learning systems usually consider student characteristics such as learning style and knowledge level to provide more personalized and adaptive student-system interaction. Methodology: A controlled experiment was conducted in a learning context with 174 subjects to evaluate the learning effectiveness of adaptivity in e-learning systems. Contribution: The main contributions of the paper are threefold. First, a novel adaptive approach is proposed based on a specific learning style model and knowledge level. Second, the approach is implemented in an e-learning system to teach computer security, the application domain. Third, a rigorous experimental evaluation of the learning effect of the adaptive approach is offered. Findings: The results indicate that adaptation according to the combination of learning style and knowledge level produces significantly better learning gains, both in the short-term and medium-term, than adaptation according to either trait individually. Recommendations for Practitioners: Practitioners should consider the combination of learning style and knowledge level when delivering and presenting learning material to their students. Recommendation for Researchers: Researchers should consider sound educational models when designing adaptive e-learning systems. Also, rigorous and carful experimental design evaluations should be taken into account. Impact on Society: Universities and e-learning industries can benefit from the proposed adaptive approach and the findings in designing and developing more personalized and adaptive e-learning systems. The incorporation of student characteristics, especially learning style and knowledge level, may be used to enhance learning. Future Research: The experiment might be duplicated with a focus on longer-term learning gains by including more subjects and more learning resources. Also, the study might be expanded to application domains other than computer security. Moreover, other variables such as student satisfaction, motivation, and affective state might be explored to further the understanding of the effect of adaptivity on learning gains.

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Section: Discussionmentioning

confidence: 99%

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…J. Brown et al, 2009;Xie et al, 2019). Consequently, more empirical research on the effect of adaptation is needed (Akbulut & Cardak, 2012;E.…”

Section: Introductionmentioning

confidence: 99%

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Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Alshammari

Qtaish

2019

JITE:Research

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Adaptive learning: Helpful to the flipped classroom in the online environment of COVID?

Clark

Kaw

Gomes

2021

Comp Applic In Engineering

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Flipped instruction in an undergraduate numerical methods course in the online, remote environment during the COVID‐19 pandemic was conducted with and without the use of adaptive‐learning lessons for pre‐class preparation. This comparison was made to explore potential differences with and without adaptive software relative to exam and concept inventory performance and student perceptions of the classroom environment, learning and motivation, and benefits and drawbacks. Student perceptions were gathered via the College and University Classroom Environment Inventory (CUCEI) and a survey designed to capture feedback specific to flipped instruction. The analysis was made possible by a current NSF grant to study adaptive learning in the flipped classroom at three universities and extensive prior research with the flipped classroom and adaptive learning by the authors. Results gathered in the online flipped classroom with adaptive learning suggested positive changes in the following: classroom environmental perceptions, preference for flipped instruction, perceived responsibility imposed, motivation for independent learning, and perceived learning. Furthermore, based on an open‐ended question, there was a significant decrease in the proportion of students who experienced load, burden, or stressors in the online flipped classroom when adaptive learning was available versus not. Multiple‐choice exam and concept‐inventory results were slightly higher with adaptive lessons (although not significantly so), with the most promising results occurring for Pell grant recipients. The emerging medical education literature has suggested that adaptive learning and flipped instruction will be key to post‐pandemic education. The present article begins advocacy for adaptive learning with flipped instruction in engineering education.

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On learning platform metrics as markers for student success in a course

Yalçin

Kaw

Clark

2023

Comp Applic In Engineering

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Adaptive learning platforms are increasingly being used as part of varying instructional modalities. Particularly relevant to this paper, adaptive learning is a critical component of personalized, preclass learning in a flipped classroom. Previously inaccessible, data generated by adaptive learning platforms regarding student engagement with the course content provides an invaluable opportunity to gain a deeper understanding of the learning process and improve upon it. We aim to investigate the relationships between adaptive learning platform interactions and overall student success in the course and identify the variables most influential to student success. We present a comprehensive analysis of our adaptive learning platform data collected in a Numerical Methods course, including aggregate statistics, frequency analysis, and Principal Component Analysis, to determine which variables exhibited the most variability and, therefore, the most information in the data. Subsequently, we used the Partitioning Around Medoids clustering approach to investigate naturally occurring clusters of students and how these clusters relate to overall performance in the course. Our results show that overall performance in the course, as measured by the final course grade, is strongly associated with (1) the behavioral interactions of students with the adaptive platform and (2) their performance on the adaptive learning assessments. We also found distinct student clusters (as defined by success in the course) that exhibited distinctly different behaviors. These findings provide qualitative and quantitative information to identify students needing support and to craft an evidence‐based support strategy for these students.

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Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017

Cited by 321 publications

References 33 publications

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Adaptive learning: Helpful to the flipped classroom in the online environment of COVID?

On learning platform metrics as markers for student success in a course

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