Manipulating Interface Design Features Affects Children's Stop-And-Think Behaviours in a Counterintuitive-Problem Game

The human–computer interaction (HCI) design of educational technologies influences cognitive behaviour, so it is imperative to assess how different HCI strategies support intended behaviour. We developed a neuroscience-inspired game that trains children's use of “stopping-and-thinking” (S&T)—an inhibitory control-related behaviour—in the context of counterintuitive science problems. We tested the efficacy of four HCI features in supporting S&T: (1) a readiness mechanic, (2) motion cues, (3) colour cues, and (4) rewards/penalties. In a randomised eye-tracking trial with 45 7-to-8-year-olds, we found that the readiness mechanic increased S&T duration, that motion and colour cues proved equally effective at promoting S&T, that combining symbolic colour with the readiness mechanic may have a cumulative effect, and that rewards/penalties may have distracted children from S&T. Additionally, S&T duration was related to in-game performance. Our results underscore the importance of interdisciplinary approaches to educational technology research that actively investigates how HCI impacts intended learning behaviours.

Predicting students’ performance in e-learning using learning process and behaviour data

E-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently, one of the methods to ensure the quality of e-learning is to use mutually independent e-learning behaviour data to build a learning performance predictor to achieve real-time supervision and feedback during the learning process. However, this method ignores the inherent correlation between e-learning behaviours. Therefore, we propose the behaviour classification-based e-learning performance (BCEP) prediction framework, which selects the features of e-learning behaviours, uses feature fusion with behaviour data according to the behaviour classification model to obtain the category feature values of each type of behaviour, and finally builds a learning performance predictor based on machine learning. In addition, because existing e-learning behaviour classification methods do not fully consider the process of learning, we also propose an online behaviour classification model based on the e-learning process called the process-behaviour classification (PBC) model. Experimental results with the Open University Learning Analytics Dataset (OULAD) show that the learning performance predictor based on the BCEP prediction framework has a good prediction effect, and the performance of the PBC model in learning performance prediction is better than traditional classification methods. We construct an e-learning performance predictor from a new perspective and provide a new solution for the quantitative evaluation of e-learning classification methods.

Distance Learning Programmes as Alternative Learning

This study aims to understand the satisfaction and experience of programme-seeking students in a community college in the United States. In order to improve the satisfaction, experience, and teaching and learning procedures of distance learning courses and programmes, it is important to understand the students' feedback and ideas. Based on the case study methodology, the researcher collected data from 1,857 inductive surveys and 11 focus group activities. This research allowed the researcher to gain knowledge and understanding about students' satisfaction, experience, and potential enrolment in degree programmes during and after the COVID-19 pandemic. More importantly, the results provide recommendations to school leaders, instructors, government leaders, and policymakers about current and future college and university development regarding changes in teaching and learning behaviours.

Facilitators Improve the Learning Experience of Visitors to a Science Centre

We studied how interactions with interpretative science centre staff impacts the learning behaviours and engagement levels of visitors who engage with exhibits at Science North (Sudbury, Canada). This study uses the Visitor-Based Learning Framework. The tool consists of seven discrete learning-associated behaviours that visitors show when engaging with exhibits, which are grouped into three categories of engagement: Initiation, Transition, and Breakthrough. These categories reflect increasing levels of engagement and depth of the learning experience. We studied forty-seven Science North exhibits, and 4,835 visitors to analyse the impact of unstructured facilitation in a naturalistic setting. We compared visitor Engagement Levels with and without a facilitator present. We determined that the presence of staff has a statistically significant impact on the percentage of visitors that engage in Breakthrough behaviours. When a facilitator is present, more visitors reach the Breakthrough Level of Engagement (p < 0.001). In the second phase of the study, we explored what facilitators do and say through thematic analysis to uncover common patterns of facilitator actions and comments. Our findings showed that facilitators employed strategies and methods that can be grouped in four categories or Facilitation Dimensions: Comfort, Information, Reflection, and Exhibit Use. These dimensions encompass different strategies and techniques of facilitation, that are used in a variety of situations and sequences. Our study goes beyond anecdotal evidence to show that staff-visitor interactions have a positive impact on visitor engagement with exhibits and therefore, potentially on visitor learning from exhibits. Our findings can be used to inform not only training programs but also managerial decisions and considerations around resource allocation. We suggest that facilitators are a fundamental asset for institutions that prioritize visitor engagement, one that should be given top priority when considering areas for investing.

THE SCHOOL AND THE TEACHER: CHANGES IN STUDENT PERCEPTION DURING THE PANDEMIC

The relationship between the learning environment and the learning behaviours has long been of interest in educational literature. When addressing the socioemotional stages, Erickson raises awareness of the psycho-social influence of school by way of diligence vs inferiority (Harwood et al., 2010), while Galos and Aldridge (2020) explore how designing a learning environment focused on student self-efficacy triggers statistically significant differences in 4 (out of 9) areas of analysis: fairness, task clarity, learning responsibility and task achievement. The aim of the present study is to highlight the significance and the differences in the main student psychosocial representations of school and teachers before and during the pandemic, the latter being characterised by government-imposed restrictions as well as changes in the student-teacher interaction, both during the second school term of 2019-2020 and the two school terms of the academic year 2020-2021. The areas we intend to explore are: overall attitude to school and student emotional states, the perception on teacher and peer relations, the perception on school as an organisation but also as a learning environment, the parents as a filter on schoolrelated perceptions, and the projective dimension on school life. The resulting statistical analysis (both nonparametric tests for independent groups and correlation) reveals major changes in the student perception on school and teachers, which will require systematic future intervention, as well as an upgrade of educational strategies, considering that the approaches designed and applied during the pandemic proved unable to compensate for the changes brought about by the restrictions on learning.