Digital competence in higher education research: A systematic literature review

In the information and knowledge society, where technology develops rapidly and penetrates deeply into our lives, the discussion about digital competence has become a hot topic today. After the emergence of the Coronavirus (Covid-19) and with its huge impact on the education industry, the concern about digital competence has reached a new height. This systematic literature review uses Web of science and Scopus as databases to store and analyze the existing research on digital competence in higher education settings. The purpose of this review is to provide the scholar community with a current overview of digital competence research from 2015 to 2021 in the context of higher education regarding the definition of digital competence, dimensions used to evaluate digital competence, research purposes, methodologies, and results and limitations. Major findings include that the majority of publications cited both research and EU policy in describing the definition of digital competence. The review indicates that most university students and teachers have a basic level of digital competence. Besides, the institutions of higher education are encouraged to focus on the development students and teachers' digital competence, create relevant learning strategies and use appropriate tools to improve the quality of education.

Identifying Supportive Student Factors for Mindset Interventions: A Two-model Machine Learning Approach

Growth mindset interventions foster students' beliefs that their abilities can grow through effort and appropriate strategies. However, not every student benefits from such interventions - yet research identifying which student factors support growth mindset interventions is sparse. In this study, we utilized machine learning methods to predict growth mindset effectiveness in a nationwide experiment in the U.S. with over 10,000 students. These methods enable analysis of arbitrarily-complex interactions between combinations of student-level predictor variables and intervention outcome, defined as the improvement in grade point average (GPA) during the transition to high school. We utilized two separate machine learning models: one to control for complex relationships between 51 student-level predictors and GPA, and one to predict the change in GPA due to the intervention. We analyzed the trained models to discover which features influenced model predictions most, finding that prior academic achievement, blocked navigations (attempting to navigate through the intervention software too quickly), self-reported reasons for learning, and race/ethnicity were the most important predictors in the model for predicting intervention effectiveness. As in previous research, we found that the intervention was most effective for students with prior low academic achievement. Unique to this study, we found that blocked navigations predicted an intervention effect as low as 0.185 GPA points (on a 0-4 scale) less than the mean. This was a notable negative prediction given that the mean intervention effect in our sample was just 0.026 GPA points, though few students (4.4%) experienced a substantial number of blocked navigation events. We also found that some minoritized students were predicted to benefit less (or even not at all) from the intervention. Our findings have implications for the design of computer-administered growth mindset interventions, especially in relation to students who experience procedural difficulties completing the intervention.

The effects of online peer-facilitated learning and distributed pair programming on students' learning

The COVID-19 pandemic has forced universities and schools around the world to adopt online learning. Teachers may wonder if their students can attain satisfactory learning performance in an online learning environment without teachers' on-the-spot attention. In order to develop students' skills in programming, promote their enjoyment of learning and intention to learn to program, the researchers integrated two innovative teaching approaches, using online peer-facilitated learning and distributed pair programming, and investigated the effects of these on students' online learning performance. This study conducted an experiment that included 128 undergraduates from four class sections of Department of Finance. Thus, the experimental design in this research was a 2 (Peer-facilitated learning vs. non-peer-facilitated learning) × 2 (Distributed pair programming vs. non-distributed pair programming) factorial pretest/post-test design. The participants in this research mainly consisted of four classes of students from a non-computer or information department who took a compulsory course on programming design.' Both quantitative and qualitative data were collected in this study. According to the results, the peer-facilitated learning group exhibited significantly better development of programming skills, enjoyment of learning, and intention to learn, than the non-peer-facilitated learning group. However, expected effects of enhancing the learning of the students in this study who received the distributed pair programming were not found. The design of online pedagogy can be a reference for online educators. The implications of applying online peer-facilitated learning and distributed pair programming to support students' learning and the design of online programming courses are discussed.

Media and information literacy as a model of societal balance: A grounded meta-synthesis

Concerns about the spread of disinformation, information disorder, and fake news have grown to unprecedented proportions in recent years. This study aimed to explore how to mitigate this communication disorder and achieve a balance in the relationship among the public, the media, the dominant institutions, and the digital influencers in society. This study used the grounded meta-synthesis method, which relies on induction, to arrive at a new model according to the objective of the study. The process of open, axial, and selective coding included 101 studies, books, reports, and guides, starting with the Public Opinion by Walter Lippmann, issued in 1922, and ending with the 2022 Edelman Trust Barometer. The results led to the proposal of a new model to reduce communication dysfunction, in which media and information literacy (MIL) plays a crucial role in increasing an individual's ability to resist disinformation and enhancing their ability to monitor the performance of institutions, as well as expanding the circle of influencers in social media. To fulfil the three goals and contribute to achieving a degree of functional balance in communication within societies, the model recommends enhancing MIL. Other intervening variables, such as the fragility of political, cultural, and legal structures, should not be disregarded.

From face-to-face to online: Teachers' perceived experiences in online distance teaching during the Covid-19 pandemic

The urgent shift to online distance teaching and learning during the Covid-19 pandemic presented teachers with unique pedagogical, technological, and psychological challenges. The aim of this study was to map the main positive and negative experiences of teachers during this transition, as well as to examine intra- and interpersonal factors that affected teachers' ability to cope effectively with the challenges of online distance teaching. We used a mixed-method approach that combined qualitative (interviews) and quantitative (questionnaires) analyses. The interviews were analyzed using a grounded theory approach, specifically a bottom-up analysis, which led to the identification of five primary categories reflecting teachers' main concerns in online distance teaching (i.e., social, emotional, cognitive, pedagogical, and system support. The two most prominent categories were pedagogy and emotions, illustrating their centrality in teachers' experiences. A regression analysis of the questionnaires' data revealed that the two main variables which predicted both positive and negative experiences in online distance teaching were self-efficacy and teachers' attitudes towards technology integration in teaching. Findings of this study allow formulation of guidelines to promote factors related to positive experiences in online distance teaching.

The influence of eXtended reality 3D visualization knowledge on future engineers' technical competencies

The rapid expansion of new technologies necessitates the adaptation of educational programs to meet the evolving demands of the labor market. This study focuses on students in technical fields, investigating their understanding of XR 3D visualization and proposing a unified training approach to improve both theoretical knowledge and practical skills. The research also evaluates how this training influences students' attitudes towards XR technologies in various applications. Key findings reveal significant improvements in technical competencies and a more positive perception of XR technologies after the training. These results suggest that targeted XR education can better prepare students for the technological challenges in their respective industries.

The effects of parent-child separation on the digital literacy of children and adolescents: A bidirectional perspective study

From a bidirectional perspective, the present cross-sectional study explored the impacts of parent-child separation on the digital literacy of children and adolescents. Drawing upon data from 1894 students (12-18 years, 49.33 % females) in Nanling county, China, we found that parent-child separation can negatively affect the digital literacy of children and adolescents, but effects differ between children experiencing parental migration or parental divorce. Parental mediation can act as a mediator in this process while children's digital feedback to parents may be considered as an auxiliary promoter. To further promote the digital literacy of children and adolescents experiencing parent-child separation, assigned tasks from adults in which children can practice knowledge and skills related to digital devices and the Internet are recommended.

Empowering Health Care Education Through Learning Analytics: In-depth Scoping Review

BACKGROUND

Digital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the "measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs."

OBJECTIVE

This scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle.

METHODS

We performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform.

RESULTS

We retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners' interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively.

CONCLUSIONS

We identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course's run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed.

Mapping the educational Frontier: Unleashing the Potential of artificial intelligence talents through cooperative planning in the Guangdong-Hong Kong-Macao greater bay area

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has become an important hub for technological innovation and economic development in China. With the growing demand for artificial intelligence (AI) and big data technology talents, it is essential to develop educational cooperation within the GBA to develop a talent pool that can meet the changing needs in the region. This paper focuses on the development of dynamic demand for AI talents and proposes a strategic planning framework for educational cooperation in the GBA. We use the research idea of common attributes and key chain clustering-factor association selection-analysis of the driving force and subordination among factors-the key characteristics of AI talents. Using collinear analysis of citations and grounded theory methods, an operational definition of the influencing factors of AI talent literacy characteristics is constructed. Using the Interpretative Structural Modeling(ISM) and MICMAC (Matrice d'Impacts Croises-Multipication Applique A Classement), analyze and identify the driving force and subordination of the influencing factors of key traits of talents, and present the combined effect of multi-level factors of key traits of talents. Combined with the educational differences and complementary advantages in the GBA, five strategies and seven implementation suggestions for the GBA's AI talent education cooperation plan are formulated to establish a collaborative ecosystem that promotes the growth and integration of AI in the GBA.

Exploring success factors and relationships in virtual learning using ISM and fuzzy MICMAC analysis

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.