The potential of immersive virtual reality to enhance learning: A meta-analysis

Research on the impact of immersive virtual reality (I-VR) technology on learning has become necessary with the decreasing cost of virtual reality technologies and the development of high-quality head-mounted displays. This meta-analysis investigates the overall effect size by combining the results of primary experimental studies that reveal the effect of I-VR on learning outcomes. Besides, effect sizes were calculated based on measuring moment, types of measurement, education level, the field of education, control group educational resources, and immersion type subgroups. One hundred five independent results were calculated from 48 primary studies published between 2016 and September 2020, including 39 randomized controlled trials and nine quasi-experimental studies. The sample size of primary studies includes 3179 students, 847 from K12, and 2332 from higher education. Random effects model was used in the calculation of effect size. As a result of the meta-analysis, it was determined that the overall effect size on the learning outcomes of I-VR was small (g = 0.38). Additionally, according to the subgroup analysis results, it was revealed that I-VR significantly differentiated effect size based on educational level, the field of education, and computer-based/traditional sources. There was no significant difference in terms of the other subgroups.     

The evaluation of school-university partnerships that improve teaching and learning practices: A systematic review

This paper presents a systematic literature review of School-University Partnerships (SUPs) that aim to promote changes in teaching and learning practices. Our analysis focuses on the evaluation of such SUPs, throughout their development process (from Exploration to Implementation and Sustainability). Our results highlight the nonlinearity of such development processes. The evaluation of the SUPs mainly relied on qualitative approaches and occurred primarily during the Implementation phase. Furthermore, the evaluation instruments used have seldom been validated or reused within the research community. Future research can focus on the development of reliable evaluation instruments, useable throughout the lifecycle of SUPs, to guide their decision-making.     

What makes or breaks competitive research proposals? A mixed-methods analysis of research grant evaluation reports

The evaluation of grant proposals is an essential aspect of competitive research funding. Funding bodies and agencies rely in many instances on external peer reviewers for grant assessment. Most of the research available is about quantitative aspects of this assessment, and there is little evidence from qualitative studies. We used a combination of machine learning and qualitative analysis methods to analyse the reviewers' comments in evaluation reports from 3667 grant applications to the Initial Training Networks (ITN) of the Marie Curie Actions under the Seventh Framework Programme (FP7). Our results show that the reviewers' comments for each evaluation criterion were aligned with the Action's prespecified criteria and that the evaluation outcome was more influenced by the proposals’ weaknesses than by their strengths.     

Reducing 0s bias in video moment retrieval with a circular competence-based captioner

The current study addresses the problem of retrieving a specific moment from an untrimmed video by a sentence query. Existing methods have achieved high performance by designing various structures to match visual-text relations. Yet, these methods tend to return an interval starting from 0s, which we named “0s bias”. In this paper, we propose a Circular Co-Teaching (CCT) mechanism using a captioner to improve an existing retrieval model (localizer) from two aspects: biased annotations and easy samples. Correspondingly, CCT contains two processes: (1) Pseudo Query Generation (captioner to localizer), aiming at transferring the knowledge from generated queries to the localizer to balance annotations; (2) Competence-based Curriculum Learning (localizer to captioner), training the captioner in an easy-to-hard fashion guided by localization results, making pairs of the false-positive moment and pseudo query become easy samples for the localizer. Extensive experiments show that our CCT can alleviate “0s bias” with even 4% improvement for existing approaches on average in two public datasets (ActivityNet-Captions, and Charades-STA), in terms of R@1,IoU=0.7. Notably, our method also outperforms baselines in an out-of-distribution scenario. We also quantitatively validate CCT’s ability to cope with “0s bias” by a proposed metric, DM. Our study not only theoretically contributes to detecting “0s bias”, but also provides a highly effective tool for video moment retrieval by alleviating such bias.