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Introduction: Higher education institutions face persistent challenges of student retention and academic progress. Personalized adaptive learning has the potential to address these issues as it leverages educational technology to tailor learning pathways according to individual student needs. Objective: To elucidate the key characteristics of personalized adaptive learning in higher education and its impact on academic performance and engagement. Methods: The Joanna Briggs Institute scoping review methodology was followed. Key international databases were searched to retrieve articles. The titles and abstracts of selected studies were imported into Covidence. Peer-reviewed journal articles, theses, and dissertations focusing on undergraduate students engaged in personalized adaptive learning, published between 2012 and 2024 were included. Data was extracted and charted in Covidence. Results were summarised through a narrative synthesis and visually presented in a PRISMA-ScR flow diagram. Results: This review included 69 eligible studies. The findings reveal insights into the multifaceted nature of personalized adaptive learning, which include platforms, implementation strategies, perceived strengths and limitations by instructors and students. Pre-knowledge quizzes were reported as the most common indicator for activating adaptive content delivery, and McGraw-Hill's Connect LearnSmart and Moodle were the most utilized adaptive platforms. Improved academic performance was reported by 41 of the studies (n = 41, 59 %), and 25 studies (n = 25, 36 %) indicated increased student engagement. Conclusion: This study highlights the potential of personalized adaptive learning to positively impact academic performance, student engagement and learning, despite technological limitations. Further research is encouraged to address technological challenges, build on strengths and refine implementation and application of personalized adaptive learning in higher education.

Mobile learning is increasingly popular due to its flexibility in timing and location. However, challenges such as small screen sizes and poor user interface design can elevate learners' cognitive load, especially extraneous cognitive load, which hinders learning. Extraneous cognitive load, stemming from user interface design complexity, must be minimized to enhance learning focus. Currently, there is no dedicated instrument for measuring extraneous cognitive load specific to mobile learning user interface design. This study aims to develop and evaluate a subjective instrument for measuring extraneous cognitive load caused by user interface design in mobile learning applications. Two sets of experiments were conducted: pretesting to establish the instrument's foundation with a small participant group, followed by pilot experiments to validate the instruments and refine experimental procedures. The NASA-TLX score was used to analyze the relationship between overall cognitive load and extraneous load across various user interface criteria. Understanding these relationships can guide user interface improvements to reduce extraneous cognitive load. Challenges encountered during pretesting and pilot experiments included participant fatigue, scale reliability issues, and incomplete data collection. To enhance reliability, adjustments were made: tasks were reduced, the scale was expanded from a 4-point to a 10-point format, and facilitators thoroughly verified data before participants concluded sessions. Creating a tool to measure how user interface design impacts users' extraneous load is important because it is the UI design, not the mobile app's content that affects extraneous load. However, general methods for measuring cognitive load may not accurately identify problems with user interface design. Therefore, an extraneous load-based method is needed. This will also eventually improve usability.

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students’ major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.

The COVID-19 pandemic's consequences have led to a global change in educational settings towards online learning. The utilization of virtual learning (VL) has increased significantly. This study aimed to extract the success factors of VL and also examine the relationships among them. The research method involves examining factors identified in the literature review and seeking confirmation from experts using the Content Validity Index (CVI) method. Ten success factors are extracted and confirmed, including Technological, Management, Learning Capability, Pedagogical, Ethical, Resource Support, Interface Design, Evaluation, Institutional, and Study Environment. Based on the Interpretive Structural Model (ISM) method and the fuzzy matrix of cross-impact multiplications applied to classification (MICMAC), which divides the factors into five levels, the relationship between these factors is examined. Level I emphasizes the importance of evaluation mechanisms. Level II stresses integrating pedagogical, ethical, resource support, and institutional aspects. Level III highlights the alignment of learner capabilities with platform interfaces. Level IV underscores the significance of the learning environment. Lastly, Level V emphasizes the interplay between technology and management in VL's expansion. The findings of this study can be developed and customized through collaboration among instructors, learners, and institutions. Moreover, the findings from correlating success factors can be applied in practical learning experiments or utilized to develop efficient modeling manuals.

With the continuous progress of society, tests, and exams appear more and more frequently in people's lives. Faced with the ever-increasing demand for test papers, efficient test paper generation algorithms have become more important. In this paper, we improved and proposed a Diseased Enhanced Genetic Algorithm (DEGA) based on the Genetic Algorithm (GA), and applied it to the test paper generation algorithm. I the crossover operator, the crossover probability that will change in different situations of the population is adopted. According to the characteristics of the test paper generation algorithm, we use the method based on the hamming distance to calculate the distance between individuals in the population. Aiming at the shortcoming that the mutation operator is too random, we designed and used a disease operator that includes three modules: natural disease, infection, and mutation. It effectively guarantees the distance between individuals in the population and improves the shortcoming that GA is easy to fall into a locally optimal solution. Finally, using the College English Test Band 4 (CET-4) questions from 2014 to 2021 as the data set, comparative experiments were carried out on the test paper generation algorithm based on Random Sampling Algorithm (RSA), GA, Enhanced Genetic Algorithm (EGA) and DEGA. The results show that when using the test paper generation algorithm based on DEGA, the generation of test papers is faster, the number of iterations is less, and the algorithm results are significantly better than other algorithms.

The COVID-19 pandemic has made a prominent impact of social contexts on teachers' professional development in remote classroom teaching. To explore how the change has altered human-environment relationships in university language classes, this qualitative case study investigated three teachers' progressive reflection on their use of affordances for teaching Chinese as a second language (L2) during COVID-19. Under the framework of human ecological language pedagogy, three themes of emergency remote teaching emerged from monthly semi-structured interviews about the three teachers’ reflective practice in remote classrooms: computer-dominant teaching conditions, flexible classroom interaction, and rational social empathy in L2 education. The findings suggest the importance of a growth mindset for L2 teachers to leverage their teaching abilities and environmental resources for continuing professional development during COVID-19 and post-pandemic periods.

The COVID-19 period forced (higher) educational institutions to come up with new ways to offer courses. This study focuses on hybrid virtual classrooms, which are learning environments that provide learning activities, guided by a teacher, to both online and onsite students simultaneously. Part-time students evaluated, based on their experiences, how hybrid virtual classrooms stimulated their feeling of being part of a community of inquiry [CoI]. They related this to the three aspects of CoI: social, teaching, and cognitive presence. Additionally, teachers' perceptions on how they enhanced the three aspects were collected. A mixed-method approach was applied in which a validated self-report questionnaire to measure the experienced CoI feeling of students was used. Qualitative data were collected through interviewing teachers about their experiences. The results illustrate that students value hybrid virtual classrooms. Students expressed that social presence can be further enhanced, especially by improving communication tools. Teachers applied various strategies to enhance social presence but felt limited by the communication tools. This also affected their opportunities to stimulate student interaction. Students felt more motivated to engage in deeper learning and felt supported by teachers in their learning process. In conclusion, students and teachers both value hybrid virtual classrooms, but enhancing social presence is challenging. To overcome this, teachers require a better understanding of meaningful learning activities to stimulate interaction between online and onsite students.

Introduction: Higher education institutions face persistent challenges of student retention and academic progress. Personalized adaptive learning has the potential to address these issues as it leverages educational technology to tailor learning pathways according to individual student needs., Objective: To elucidate the key characteristics of personalized adaptive learning in higher education and its impact on academic performance and engagement., Methods: The Joanna Briggs Institute scoping review methodology was followed. Key international databases were searched to retrieve articles. The titles and abstracts of selected studies were imported into Covidence. Peer-reviewed journal articles, theses, and dissertations focusing on undergraduate students engaged in personalized adaptive learning, published between 2012 and 2024 were included. Data was extracted and charted in Covidence. Results were summarised through a narrative synthesis and visually presented in a PRISMA-ScR flow diagram., Results: This review included 69 eligible studies. The findings reveal insights into the multifaceted nature of personalized adaptive learning, which include platforms, implementation strategies, perceived strengths and limitations by instructors and students. Pre-knowledge quizzes were reported as the most common indicator for activating adaptive content delivery, and McGraw-Hill's Connect LearnSmart and Moodle were the most utilized adaptive platforms. Improved academic performance was reported by 41 of the studies (n = 41, 59 %), and 25 studies (n = 25, 36 %) indicated increased student engagement., Conclusion: This study highlights the potential of personalized adaptive learning to positively impact academic performance, student engagement and learning, despite technological limitations. Further research is encouraged to address technological challenges, build on strengths and refine implementation and application of personalized adaptive learning in higher education., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Eileen du Plooy reports Covidence systematic review software was provided by Research Masterminds Success Academy. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Mobile learning is increasingly popular due to its flexibility in timing and location. However, challenges such as small screen sizes and poor user interface design can elevate learners' cognitive load, especially extraneous cognitive load, which hinders learning. Extraneous cognitive load, stemming from user interface design complexity, must be minimized to enhance learning focus. Currently, there is no dedicated instrument for measuring extraneous cognitive load specific to mobile learning user interface design. This study aims to develop and evaluate a subjective instrument for measuring extraneous cognitive load caused by user interface design in mobile learning applications. Two sets of experiments were conducted: pretesting to establish the instrument's foundation with a small participant group, followed by pilot experiments to validate the instruments and refine experimental procedures. The NASA-TLX score was used to analyze the relationship between overall cognitive load and extraneous load across various user interface criteria. Understanding these relationships can guide user interface improvements to reduce extraneous cognitive load. Challenges encountered during pretesting and pilot experiments included participant fatigue, scale reliability issues, and incomplete data collection. To enhance reliability, adjustments were made: tasks were reduced, the scale was expanded from a 4-point to a 10-point format, and facilitators thoroughly verified data before participants concluded sessions. Creating a tool to measure how user interface design impacts users' extraneous load is important because it is the UI design, not the mobile app's content that affects extraneous load. However, general methods for measuring cognitive load may not accurately identify problems with user interface design. Therefore, an extraneous load-based method is needed. This will also eventually improve usability., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners’ performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics.

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students' major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

The COVID-19 pandemic's consequences have led to a global change in educational settings towards online learning. The utilization of virtual learning (VL) has increased significantly. This study aimed to extract the success factors of VL and also examine the relationships among them. The research method involves examining factors identified in the literature review and seeking confirmation from experts using the Content Validity Index (CVI) method. Ten success factors are extracted and confirmed, including Technological, Management, Learning Capability, Pedagogical, Ethical, Resource Support, Interface Design, Evaluation, Institutional, and Study Environment. Based on the Interpretive Structural Model (ISM) method and the fuzzy matrix of cross-impact multiplications applied to classification (MICMAC), which divides the factors into five levels, the relationship between these factors is examined. Level I emphasizes the importance of evaluation mechanisms. Level II stresses integrating pedagogical, ethical, resource support, and institutional aspects. Level III highlights the alignment of learner capabilities with platform interfaces. Level IV underscores the significance of the learning environment. Lastly, Level V emphasizes the interplay between technology and management in VL's expansion. The findings of this study can be developed and customized through collaboration among instructors, learners, and institutions. Moreover, the findings from correlating success factors can be applied in practical learning experiments or utilized to develop efficient modeling manuals., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

With the continuous progress of society, tests, and exams appear more and more frequently in people's lives. Faced with the ever-increasing demand for test papers, efficient test paper generation algorithms have become more important. In this paper, we improved and proposed a Diseased Enhanced Genetic Algorithm (DEGA) based on the Genetic Algorithm (GA), and applied it to the test paper generation algorithm. I the crossover operator, the crossover probability that will change in different situations of the population is adopted. According to the characteristics of the test paper generation algorithm, we use the method based on the hamming distance to calculate the distance between individuals in the population. Aiming at the shortcoming that the mutation operator is too random, we designed and used a disease operator that includes three modules: natural disease, infection, and mutation. It effectively guarantees the distance between individuals in the population and improves the shortcoming that GA is easy to fall into a locally optimal solution. Finally, using the College English Test Band 4 (CET-4) questions from 2014 to 2021 as the data set, comparative experiments were carried out on the test paper generation algorithm based on Random Sampling Algorithm (RSA), GA, Enhanced Genetic Algorithm (EGA) and DEGA. The results show that when using the test paper generation algorithm based on DEGA, the generation of test papers is faster, the number of iterations is less, and the algorithm results are significantly better than other algorithms., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

The COVID-19 pandemic has made a prominent impact of social contexts on teachers' professional development in remote classroom teaching. To explore how the change has altered human-environment relationships in university language classes, this qualitative case study investigated three teachers' progressive reflection on their use of affordances for teaching Chinese as a second language (L2) during COVID-19. Under the framework of human ecological language pedagogy, three themes of emergency remote teaching emerged from monthly semi-structured interviews about the three teachers' reflective practice in remote classrooms: computer-dominant teaching conditions, flexible classroom interaction, and rational social empathy in L2 education. The findings suggest the importance of a growth mindset for L2 teachers to leverage their teaching abilities and environmental resources for continuing professional development during COVID-19 and post-pandemic periods., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author.)

The COVID-19 period forced (higher) educational institutions to come up with new ways to offer courses. This study focuses on hybrid virtual classrooms, which are learning environments that provide learning activities, guided by a teacher, to both online and onsite students simultaneously. Part-time students evaluated, based on their experiences, how hybrid virtual classrooms stimulated their feeling of being part of a community of inquiry [CoI]. They related this to the three aspects of CoI: social, teaching, and cognitive presence. Additionally, teachers' perceptions on how they enhanced the three aspects were collected. A mixed-method approach was applied in which a validated self-report questionnaire to measure the experienced CoI feeling of students was used. Qualitative data were collected through interviewing teachers about their experiences. The results illustrate that students value hybrid virtual classrooms. Students expressed that social presence can be further enhanced, especially by improving communication tools. Teachers applied various strategies to enhance social presence but felt limited by the communication tools. This also affected their opportunities to stimulate student interaction. Students felt more motivated to engage in deeper learning and felt supported by teachers in their learning process. In conclusion, students and teachers both value hybrid virtual classrooms, but enhancing social presence is challenging. To overcome this, teachers require a better understanding of meaningful learning activities to stimulate interaction between online and onsite students., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners' performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics., Competing Interests: The authors declare no conflict of interest., (© 2021 The Author. Published by Elsevier Ltd.)