Education and Information Technologies最新文献

An exploratory sequential mixed-method study is designed to develop and test a comprehensive model explaining the relationships between factors associated with smartphone addiction and high school students’ academic achievement. Involving two main phases of qualitative and quantitative, focus group discussions with high school students and interviews with teachers were conducted in the first phase, both to decide on the factors and to reveal the relationships between the factors in the model. According to the qualitative results, the most important factors were found as social media usage, cyberloafing, academic procrastination, external and internal academic locus of control. Then, a hypothesis model involving these factors was developed to explain high school students' academic achievement. Obtained from 410 high school students, quantitative data are collected online by the use of some scales measuring the factors included in the model. Using the path analysis method, the hypothesis model was tested, and it was observed that the model fit the data well. As a result, the factors that most influence academic achievement were found as duration of social media usage, external academic locus of control, smartphone addiction, internal academic locus of control, academic procrastination, and cyberloafing respectively. The uniqueness of the presented model in this research is believed to lie in its holistic perspective on the relationships between smartphone addiction and related factors, and their effects on academic achievement. Looking from a big picture, this model is expected to provide a roadmap for practitioners and decision-makers in terms of how to improve students’ academic achievement.

This study investigates the effects of online instructors' use of initiation and maintenance rapport-building strategies (RBS) on Chinese EFL learners' CALL motivation and cognitive load management. Mixed methods research was used to concurrently triangulate different strands of data on the effects of RBS on 86 randomly sampled EFL learners. The interpretations were made based on methodological triangulation of QUAN + QUAL data and running one-way ANOVA. The results show that the interactive use of initiation and maintenance RBS is statistically significant in enhancing CALL motivation (F(1,84) = 23.45, p < .001) and lowering cognitive load (F(1,84) = 17.89, p < .001) compared to isolated use of these strategies. The study reveals that teachers' interactive use of RBS enhances learners' CALL motivation by increasing learning interests (mean difference = 1.98, p < .001), catering to dynamic motivational needs (mean difference = 1.64, p < .001), and creating a learner-friendly environment (mean difference = 1.42, p < .001). Moreover, the study confirms the psychological effects of interactive use of RBS in lowering cognitive load through information-sharing (mean difference = 1.89, p < .001), common-grounding (mean difference = 1.56, p < .001), and personalized instruction strategies (mean difference = 1.34, p < .001) during the CALL course. The implications of the study inform researchers and practitioners of CALL about the efficacy of using a static and flexible combination of initiation and maintenance RBS to enhance EFL learners' motivation and cognitive load.

Virtual reality (VR) has gained prominence and a growing prevalence. However, parental attitudes toward using VR for educational purposes (i.e., educational VR (EVR)) are under-researched. This study investigates parents’ perceptions and experiences regarding the use of EVR, focussing on mathematics learning. Using the technology acceptance model (TAM) as a guiding framework, qualitative interviews with parents and their children from ten families (15 interviewees) were conducted. The participants interacted with a VR serious game, solving 12 mathematical problems, followed by interviews. Qualitative data analysis reveals four main factors influencing parents’ acceptance of educational VR: cognitive-developmental, affective-physical, technical, and contextual/chronological aspects. The original TAM's positive factors (usefulness and ease) are validated, while this study extends the model to include new positive (e.g., interest) and negative factors (e.g., health risks) specific to VR. Novel contributions to the literature include controversial factors (e.g., preference for 2D or 3D and mental effects) and positive factors (e.g., pandemic context, parent–child perspectives, and manipulatives). These findings not only contribute to the advancement of theory in EVR acceptance, but also provide valuable information for EVR design, parental involvement in educational initiatives, and informing educators and policymakers about diverse perspectives on educational technology.

In the rapidly evolving landscape of higher education and adult learning, asynchronous video-based online learning has not only become the new norm but has also emerged as the cornerstone of instructional delivery for Massive Open Online Courses (MOOCs). Despite its widespread adoption, this learning mode confronts a critical challenge: the inherent lack of social presence, posing a significant risk of diminishing learner affective engagement and, consequently, jeopardizing the efficacy of learning outcomes. Addressing this pressing issue, our study conducted a comprehensive analysis of 240 instructional videos from 240 distinct instructors on a MOOC platform, supplemented by 845 post-course learner feedback surveys from a diverse cohort of college students and adult learners. Using deep learning and statistical analysis, the research revealed that the on-screen presence of instructors does not inherently affect students’ affective engagement. The study revealed that learners’ affective engagement is affected by distinct combinations of the instructor’s facial and paraverbal expressions, including happiness, surprise, and anger, which vary depending on whether the instructor is visible. The discovery that vocal attractiveness is a pivotal element in enhancing learners’ affective engagement with instructional videos marks a paradigm shift in our understanding of digital andragogy and heutagogy. This study propels academic discourse by illuminating the critical role of instructor non-verbal cues in establishing social presence and facilitating emotional contagion within asynchronous video-based online learning but also provides educators and content creators with empirically-backed techniques to revolutionize video instruction and amplify affective engagement.

The current study was designed to investigate how varied time of day (ToD) could affect the acquisition of basketball game actions from different video formats. First-year physical education students (i.e., novice practitioners) were quasi-randomly assigned to three experimental conditions: continuous video, macro-segmented video, or micro-segmented video. Morning (between 08:00 and 09:00) and late afternoon (between 16:00 and 17:00) sessions were conducted, involving study phases (i.e., observation of the scenes of play) and immediate-recall tests (i.e., game comprehension test and game performance test). Oral temperature and mood states were also collected at the beginning of each session. Compared to the morning, the results revealed that afternoon hours resulted in higher oral temperature, higher negative mood states (e.g., anxiety and fatigue), and lower positive mood (i.e., vigor) in all experimental conditions. For both short-term learning tests, the results showed that: (i) students performed significantly better in the morning rather than in the late afternoon (regardless of video formats), (ii) students exposed to the two segmented videos performed better than those exposed to a continuous video at the two ToD (without any significant difference between macro-segmented and micro-segmented videos). The findings highlight the morning’s superiority in the acquisition of motor knowledge from different video formats, due to mood disturbances and lower arousal levels. The present experiment encourage basketball teachers to use segmented videos, especially in the morning, to explain tactical information for novices.

Learning analytics intervention (LAI) studies aim to identify at-risk students early during an academic term using predictive models and facilitate educators to provide effective interventions to improve educational outcomes. A major impediment to the uptake of LAI is the lack of access to LAI infrastructure by educators to pilot LAI, which typically requires substantial institution-wide efforts and investment to collect related data sets and develop accurate predictive models that identify at-risk students and also provide tools to facilitate interventions. This paper presents a novel LAI framework, termed Student Performance Prediction and Action (SPPA), that facilitates educators to seamlessly provide LAIs in their courses avoiding the need for large-scale institution-wide efforts and investments. Educators develop course-specific predictive models using historical course assessment data. In learning analytics, providing effective interventions is a challenge. SPPA utilises pedagogy principles in course design and interventions to facilitate effective interventions by providing insights into students’ risk levels, gaps in students’ knowledge, and personalised study/revision plans addressing knowledge gaps. SPPA was evaluated in a large undergraduate course on its ability to predict at-risk students and facilitate effective interventions as well as its ease of use by academics. The results are encouraging with high performance of predictive models, facilitating effective interventions leading to significant improved educational outcomes with positive feedback and uptake by academics. With its advantages, SPPA has the potential to catalyse and influence wide-scale adoption in LAIs.

This study aims to examine the temporal evolution and changes of research interests and trends in the educational augmented reality (AR) literature. To this end, 3718 articles published in the 2003–2022 period and indexed in the Scopus database were analyzed through machine learning-based semantic topic modeling and descriptive analysis. The findings indicate a notable upswing in studies on educational AR, particularly since 2015. The articles were categorized into eleven primary themes through topic modeling analysis. The three most prevalent topics in terms of volume are “Augmented Reality in Education and Cultural Heritage”, “Medical Education and Patient Care”, and “Enhancing Safety and Information in Food Consumption”. Observations across different times reveal that “Augmented Reality in Electrical and Electronic Systems” and “Gesture-Based Instruction and Maintenance” were studied in the initial periods. Since 2015, there has been a notable increase in applications falling under the “Serious Games” category. The least voluminous and slowest-evolving topics are identified as “Serious Games for Children with Autism Spectrum Disorder”, “Augmented Reality in Chemistry and Biology Laboratories”, and “Augmented Reality for Safe and Efficient Driving”. Considering the recent momentum gained by these topics, it is anticipated that they will become popular topics for future studies. This study represents a significant milestone as the first and most comprehensive research using machine learning in its field, not only explaining the current state of the field but also providing valuable information for future research efforts.

The utilization of artificial intelligence (AI) among students is rapidly gaining prominence worldwide. However, Malaysia lags in terms of research and information in this area. This quantitative study aims to identify the factors that contribute to the adoption of AI among postgraduate students. The study focuses on the postgraduate student population in Malaysia, employing convenience sampling techniques. The research findings reveal that two variables, namely hedonistic and habit, significantly influence the adoption of AI among postgraduate students. These findings are expected to provide valuable insights to stakeholders for future implementation of AI among postgraduate students. By understanding the key factors influencing AI adoption, relevant parties can effectively strategize and enhance the utilization of AI technology in the postgraduate education landscape.

There is a growing interest in whether social robots, which are embodied and exhibit human-like behaviour, can be used for teaching and learning. Still, very few studies focus on the teacher’s role. This study focuses on how a teacher acted in a learning-by-teaching activity with 20 children. In this small-scale field experiment, the teacher’s interactions and teaching actions were observed when the teacher scaffolded a learning activity where children played a collaborative digital mathematics game to strengthen their mathematical reasoning and conceptual understanding of arithmetic. When playing, the children were acting as tutors for a tutee, according to the learning-by-teaching principle. In one scenario, the tutee was a younger child; in the other, the tutee was a social robot. Twenty 30-minute game-playing sessions are observed, video-recorded, and transcribed. The study explores the teacher’s interactions and teaching actions in the two scenarios and discusses the results from the perspective of the teacher’s role, social norms, and teacher digital competence. The interaction and thematic analyses show similarities and characteristic differences in the teacher’s interaction patterns in the two scenarios. The teaching actions are similar on a structural level and differ regarding the types and distribution of teaching actions. In the child-child scenario, the teacher directs most teaching actions to both players, and the actions are didactic (mathematical) scaffolding. In contrast, in the child-robot scenario, the teacher only addresses the tutor, and the scaffolding is socially oriented. Implications for a teaching practice involving social robots as learning companions are discussed regarding teachers’ presence and participation, types of social robot knowledge that go beyond digital competence, and new challenges introduced by using social robots as learning companions in the classroom. The study contributes new insights into the teacher’s role and actions when teaching with a social robot in a collaborative learning situation, which is relevant for educational research and teaching practice.

Concepts and practices are widely used to assess students’ development in computational thinking (CT). However, less is known about how the development of each construct relates to that of the other. With a sample of 997 grade 6 students (average age = 11.43 at the beginning of the school year) from 14 primary schools, we examined the co-development of students’ CT concepts and practices based on the research method commonly used in the literature on conceptual and procedural knowledge for studying the relations between the two constructs. We first designed an instrument to measure CT concepts using an evidence-based approach, and validated it through item response theory. By applying multidimensional item response theory, we confirmed the four dimensions of CT practices using an instrument applied in a previous study. We found that after one year of CT learning, the students had improved in terms of both concepts and practices, indicating the effectiveness of the CT intervention. Structural equation modelling indicated that CT concepts and practices facilitated each other’s development; practices played a particularly important role in facilitating the development of CT. Our study provides solid evidence for the importance of the problem-solving aspect of CT as reflected in the dimension of practices. It also provides valuable insights for future pedagogical models, such as allocating sufficient time for students to practise in programming tasks.