In recent years, the integration of cutting-edge technology into professional sports training (ST) has revolutionized the way athletes prepare for competition. The study aims to quantitatively analyse the impact of cutting-edge technology on enhancing performance, efficiency, and outcomes in professional ST programs.
The purpose of this study is to investigate how advanced technologies influence training effectiveness in professional sports, with a focus on performance enhancement, injury prevention, and overall athlete development.
This study utilized a quantitative descriptive research design to assess technology's impact on ST. A sample of 450 sports professionals, selected through purposive sampling, completed structured questionnaires. Data were analysed using SPSS to identify correlations between technology use and training outcomes.
The research findings indicate that athletes utilizing virtual reality (VR), AR, and wearable technology (WT) in their training exhibit significant improvements in performance metrics (PM) and a reduction in injury risks compared to those relying on traditional training methods.
The study results demonstrate that technology-assisted training improves cognitive skills (CS), decision-making, and psychological aspects like motivation and focus. Additionally, advanced analytics and artificial intelligence (AI) enable more effective, personalized coaching strategies, enhancing overall training outcomes.
This study's contribution lies in its novel approach, demonstrating virtual and augmented reality, wearable tech, and advanced analytics enhance ST by improving performance, CS, and resilience, while also reducing injuries and speeding up recovery.
This research significantly advances the sports science domain by offering empirical evidence supporting technology integration, filling prevalent gaps in the current literature.
Augmented reality (AR) emerges as a technology with considerable promise and substantial potential for pedagogical integration within language education contexts. However, there remains a scarcity of review studies exploring the best practices and principles for oral communication facilitation based on robust theoretical models or research-proven theories.
This paper aims to explore the major constituents for achieving the successful outcomes of AR-assisted oral communication, grounded in the theory-based design model, and formulate theoretical guidelines to enhance the best practice facilitation process.
Starting from 255 sources, 25 studies from the ISI Web of Science were selected through rigorous screening processes based on the guidelines of the PRISMA statement. Furthermore, the researchers adopted qualitative content analysis and complementary quantitative descriptive analysis for data synthesis in accordance with four key variables of the established model.
The results showed that the guided learner-directed learning approach supported by constructionists and reflective learning mechanisms was identified as an effective pedagogical design. Additionally, educational practitioners could employ hybrid learning strategies, including situated, collaborative, self-regulated and game-based learning strategies, to facilitate effective verbal communication. Finally, besides augmenting location-based content and facilitating contextual information and discussion, AR designers were advised to integrate personalised features such as self-monitoring and evaluating and learner manipulation of virtual objects into system development to promote learners' locus of control and embodied cognition.
Despite the numerous positive effects of augmented reality (AR) on learning, previous research has shown ambiguous results regarding the cognitive demand on the learner arising from, for example, the overlay of virtual elements or novel interaction techniques. At the same time, the number of evidence-based guidelines on designing AR is limited or focuses on global effects, primarily relying on media comparison studies, whose validity is criticised.
To guide the meaningful design of learning and training settings, this paper systematically reviews empirical research on AR design and synthesises the findings to develop evidence-based recommendations for designing AR systems considering cognitive load.
We conducted a systematic literature review, initially screening 810 distinct papers and ultimately analysing findings from 27 publications, which report on 29 distinct experimental studies. This selection was based on rigorously defined inclusion and exclusion criteria, adhering to the PRISMA guidelines.
The central value of this paper is the aggregation of existing evidence from empirical studies, resulting in 15 recommendations for AR design based on six design dimensions: Spatiality-related, Interaction-related, Contextuality-related, Content-related, Guidance-related and Display Selection. Additionally, with three points for future research, this systematic literature review, first, stresses the need for more empirical evidence and value-added studies. Second, learner characteristics that might influence cognitive load in AR-based learning should be examined. Third, it advocates for the inclusion of measurements beyond the NASA-TLX, and including more physiological measurements (e.g., eye-tracking, EEG) to enhance the applicability of the results for learning and training situations.
Students experience higher-order thinking skills by finding ways to solve the problem, debugging errors while applying the solution, and testing the solution in programming. However, the inability to create schemas that will characterise programming structures is one of the difficulties during this process.
This study aimed to create an augmented reality-based programming editor and evaluate its contributions to learning programming. Text-based programming codes developed by the students on the editor were instantly visualised through a traffic metaphor in dynamic visualisation via augmented reality. The findings of this study highlight the significance of a novel usage of augmented reality in shaping cognitive processes during the programming learning process.
An exploratory case study was adopted in the study. Participants voluntarily selected 15 students in the same secondary school's 4th and 5th grades. The data to determine the programming performances were collected via a programming performance test and analysed with the rubric created by the authors. Interviews and screen recordings were employed to determine the factors that had a role in the programming learning process.
The results indicated that teaching programming using the augmented reality-based editor has positively contributed to learning programming. The editor's scenario, animation, and error display features incorporated with an authentic dynamic visualisation were remarkable in this contribution. The editor also contributed to reducing students' efforts to create schemas for programming by bringing the context closer to reality throughout the programming process.
Both eye movement modelling examples (EMMEs) and cues guide attention and improve learning in multimedia learning environments. EMME can act as a special form of cue. However, no studies have directly examined whether EMME would be superior to visual cues.
The study was to investigate whether there were advantages of EMME over cues on visual attention, learning outcomes and subjective perception in a multimedia environment.
In Experiment 1, 67 college students were randomly assigned to the EMME group, cue group and control group and learned from animations explaining the synaptic transmission. In Experiment 2, we replicated Experiment 1 using longer learning material. Seventy-one college students were randomly assigned to three groups (EMME, cue and control) and learned from animations explaining the process of cell division.
In Experiment 1, results indicated both EMMEs and cues guide attention and facilitate learning. Regarding learning outcomes and subjective perception, learners did not benefit more from EMMEs than cues. Eye movement indicators showed that EMMEs were better than cues. In Experiment 2, results were consistent with Experiment 1.
This study is the first to compare EMMEs and cues. We found that EMMEs and cues were equivalent in producing effects on learning outcomes and subjective perception. EMMEs were superior to cues in guiding attention and conveying strategies, as indicated by eye movements. Both theoretical and practical implications could be drawn.
Process data captured by computer-based assessments provide valuable insight into respondents' cognitive processes during problem-solving tasks. Although previous studies have utilized process data to analyse behavioural patterns or strategies in problem-solving tasks, the connection between latent cognitive states and their theoretical interpretation in problem solving remains unclear.
This research aims to investigate the connections between similar hidden response states and unfold respondents' transition paths in problem-solving processes. Analysing process data from the 2012 United States Programme for the International Assessment of Adult Competencies (PIAAC), this study seeks to discern patterns in problem solving among participants.
The hidden Markov model was first used to uncover the hidden states based on a sequence of observed actions. Next, Gaussian graphical network analysis was employed to analyse the relationships between hidden response states.
Results indicated that correct responders had simpler, clearer state relationships, while incorrect responders displayed more complex connections. Respondents who solved the tasks correctly had clearer thoughts about the problem-solving process, whereas incorrect respondents struggled to understand the problem and failed to figure out solutions. Cognitive state changes during problem solving also varied between groups. The correct groups showed cohesive, logical transitions, in contrast to the emerged isolated, erratic patterns of the incorrect groups.
Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress. Optional assessments may encourage voluntary engagement, potentially leading to a more genuine reflection of student understanding. Also, frequent assessments provide continuous opportunities for feedback and adjustment, which can keep students actively engaged in the learning process.
This study aims to investigate two crucial facets of formative assessments: frequency and the level of stakes involved (mandatory vs. optional). We examine how modifying the frequency of formative assessments affects students' course performance. Additionally, we evaluate the impact of mandatory versus optional formative assessments on students' course performance in higher education.
The sample of this study consisted of undergraduate students (n = 336) enrolled in three sections of a large asynchronous course at a Canadian university. We extracted features associated with online formative assessments (e.g., the number of attempts and average scores) from the learning management system. Next, we used these features to predict students' performance in summative assessments (two midterms and a final exam).
Our findings indicated that increasing the frequency of online formative assessments did not consistently improve student performance. Also, participation frequency in online formative assessments seemed to vary depending on assessment stakes (i.e., optional vs. mandatory). We recommend that instructors examine what conditions can maximize the contribution of formative assessments to students' academic achievement before building predictive models.
Language assessment plays a pivotal role in language education, serving as a bridge between students' understanding and educators' instructional approaches. Recently, advancements in Artificial Intelligence (AI) technologies have introduced transformative possibilities for automating and personalising language assessments.
This article aims to explore the design and implementation of AI-enabled assessment tools in language education, filling the research gaps regarding the impact of assessment type, intervention duration, education level, and first language learner/second language learner (L1/L2) on the effectiveness of AI-enabled assessment tools in enhancing students' language learning outcome.
This study conducted a systematic review and meta-analysis to examine 25 empirical studies from January 2012 to March 2024 from six databases (including EBSCO, ProQuest, Scopus, Web of Science, ACM Digital Library and CNKI).
The predominant design in AI-driven assessment tools is the structural AI architecture. These tools are most frequently deployed in classroom settings for upper primary students within a short duration. A subsequent meta-analysis showed a medium overall effect size (Hedges's g = 0.390, p < 0.001) for the application of AI-enabled assessment tools in enhancing students' language learning, underscoring their significant impact on language learning outcomes. This evidence robustly supports the practical utility of these tools in educational contexts.
The analysis of several moderator variables (i.e., assessment type, intervention duration, educational level and L1/L2 learners) and potential impacts on language learning performance indicates that AI-enabled assessment could be more useful in language education with a proper implementation design. Future research could investigate diverse instructional designs for integrating AI-based assessment tools in language education.
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