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Studies have suggested that virtual reality (VR) as an ‘empathy-making machine’ has the potential to trigger historical empathy. Different VR technologies offer different levels of immersion with different educational implications. Higher immersive VR technology has shown promise in enhancing learners’ empathy levels. However, how highly immersive VR promotes learners’ historical empathy in real-life classroom settings has not been explored. Hence, this study focuses on comparing two levels of immersive VR-supported learning for historical empathy acquisition among secondary learners. A total of 49 students were recruited. A quasi-experimental design was employed and the quantitative and qualitative data were analyzed to explore participants’ historical knowledge and empathy. The quantitative data show no significant difference between immersive virtual reality (IVR) approach-supported learning and flatscreen VR-supported learning. However, the qualitative analysis suggests that the students from the IVR group showed stronger ‘physical empathy’. Intriguingly, even though the tests did not demonstrate that the students learned more in IVR-supported learning, they self-reported that they learned better in IVR-supported learning. The paper identifies implications for the connection between ‘physical empathy’ and the level of immersive technology.

Writing proficiency is an essential skill for upper secondary students that can be enhanced through effective feedback. Creating feedback on writing tasks, however, is time-intensive and presents a challenge for educators, often resulting in students receiving insufficient or no feedback. The advent of text-generating large language models (LLMs) offers a promising solution, namely, automated evidence-based feedback generation. Yet, empirical evidence from randomized controlled studies about the effectiveness of LLM-generated feedback is missing. To address this issue, the current study compared the effectiveness of LLM-generated feedback to no feedback. A sample of N = 459 upper secondary students of English as a foreign language wrote an argumentative essay. Students in the experimental group were asked to revise their text according to feedback that was generated using the LLM GPT-3.5-turbo. The control group revised their essays without receiving feedback. We assessed improvement in the revision using automated essay scoring. The results showed that LLM-generated feedback increased revision performance (d = .19) and task motivation (d = 0.36). Moreover, it increased positive emotions (d = 0.34) compared to revising without feedback. The findings highlight that using LLMs allows to create timely feedback that can positively relate to students’ cognitive and affective-motivational outcomes. Future perspectives and the implications for research and practice of using LLM-generated feedback in intelligent tutoring systems are discussed.

Previous studies reported that acquiring computer programming skills is challenging and might result in high dropout rates. A quasi-experimental design was used to examine the role of different factors in dropping out of an e-based computer programming course. This study applied a knowledge in programming assessment test (20 multiple-choice questions covering the following topics: variables, loops, conditionals, functions, and general knowledge of Python), The Learning Motivating Factors Questionnaire, The Big Five-2, and The Basic Psychological Need Satisfaction & Frustration Scale. Ninety-four participants (38 males and 56 females) completed the course, while 305 participants started it. The mean age of e-learners was 29.96 years (SD 8.27), age range = 18 to 54. The results showed that e-learners who completed the course had higher initial knowledge assessment scores than those who dropped out after the first assessment. Reward and recognition as a motivator were significantly higher in males who completed the course than those who dropped out after the second knowledge assessment. Extraversion was significantly lower in females who completed the course than those who dropped after the first or second knowledge assessment test. Relatedness frustration was significantly higher in those who dropped out after the first knowledge assessment. Due to significant limitations of the sample size, cultural context, measures applied, and research design, the findings would preferably be regarded with caution.

Digital media are widely used among Higher Education (HE) students; however, it is not clear yet how students from different disciplines apply learning technology. When differences in technology use were found among disciplines, they were identified broadly, for example, between soft and hard sciences, that is, social sciences/humanities vs. natural sciences. Beyond this, disciplines may also differ according to their traditions, norms and values that form their discipline-specific cultures. Thus, based on the theoretical perspectives of social cognitive theory and organisational culture, this present study aimed to examine students’ technology use in different HE disciplines. In a multilevel analysis, data from four German universities were analyzed, including more than 1,200 students from 105 disciplines. The findings suggest that students in different disciplines vary substantially in their average use of study-related technology. Furthermore, on the individual level, study-related technology use depends on individual factors, e.g., perceived usefulness. This present study's results contribute to further extending our knowledge about student technology use in HE. Above all, the conceptual and methodical models can serve as a theoretical and empirical basis for future studies. To analyze disciplinary differences, the study shows that a rough classification, for example hard versus soft disciplines, is not sufficient. Instead, the different disciplines need to be distinguished.

With the continuous progress of society, tests, and exams appear more and more frequently in people's lives. Faced with the ever-increasing demand for test papers, efficient test paper generation algorithms have become more important. In this paper, we improved and proposed a Diseased Enhanced Genetic Algorithm (DEGA) based on the Genetic Algorithm (GA), and applied it to the test paper generation algorithm. I the crossover operator, the crossover probability that will change in different situations of the population is adopted. According to the characteristics of the test paper generation algorithm, we use the method based on the hamming distance to calculate the distance between individuals in the population. Aiming at the shortcoming that the mutation operator is too random, we designed and used a disease operator that includes three modules: natural disease, infection, and mutation. It effectively guarantees the distance between individuals in the population and improves the shortcoming that GA is easy to fall into a locally optimal solution. Finally, using the College English Test Band 4 (CET-4) questions from 2014 to 2021 as the data set, comparative experiments were carried out on the test paper generation algorithm based on Random Sampling Algorithm (RSA), GA, Enhanced Genetic Algorithm (EGA) and DEGA. The results show that when using the test paper generation algorithm based on DEGA, the generation of test papers is faster, the number of iterations is less, and the algorithm results are significantly better than other algorithms.

The internet provides up-to-date, multi-dimensional and spatial information on geographical conflicts worldwide. Internet searches can therefore be used in geography education for students to learn about geographical conflicts and to form their own opinions on them. However, previous research indicates that students lack competences in searching and evaluating digital information on geographical conflicts and also in processing the information found argumentatively. Therefore, we developed a digital learning unit, available as Open Educational Resource, to train upper secondary and university students in targeted strategies for conducting internet searches on geographical conflicts and developing subsequent argumentations. In this study, we investigated whether geographic internet searches and argumentation skills can be promoted using the learning unit. For this purpose, we analysed the work products of 39 students that they produced within the learning unit, consisting of an internet search on a geographical conflict and a subsequent argumentation. In addition, the students evaluated the learning unit using digitally provided questionnaires. A comparison of the students’ work results with previous research shows that the learning unit does not seem to affect the amount of information students find on the internet, but students seemed to engage more intensively with the information found. Using the information from the internet, students were able to create multi-perspective argumentations in which they could defend their opinions with justifications, counter-arguments and rebuttals. The results of our study thus show that internet searches on geographical conflicts and the development of subsequent argumentation can be promoted with the digital learning unit.

Brain–computer interface (BCI) technology has the potential to positively contribute to the educational learning environment, which faces many challenges and shortcomings. Cognitive and affective BCIs can offer a deep understanding of brain mechanisms, which may improve learning strategies and increase brain-based skills. They can offer a better empirical foundation for teaching–learning methodologies, including adjusting learning content based on brain workload, measuring student interest of a topic, or even helping students focus on specific tasks. The latest findings from emerging BCI technology, neuroscience, cognitive sciences, and psychology could be used in learning and teaching strategies to improve student abilities in education. This study investigates and analyzes the research on BCI patterns and its implementation for enhancing cognitive capabilities of students. The results showed that there is insufficient literature on BCI that addresses students with disabilities in the learning process. Further, our analysis revealed a bias toward the significance of cognitive process factors compared with other influential factors, such as the learning environment and emotions that influence learning. Finally, we concluded that BCI technology could improve students’ learning and cognitive skills—when consistently associated with the different pedagogical teaching–learning strategies—for better academic achievement.

The recent pandemic brought on considerable changes in terms of learning activities, which were moved from in-person classroom-based lessons to virtual work performed at home in most world regions. One of the most considerable challenges faced by educators was keeping students motivated toward learning activities. Interactive learning environments in general, and augmented reality (AR)-based learning environments in particular, are thought to foster emotional and cognitive engagement when used in the classroom. This study aims to compare the motivation and learning outcomes of middle school students in two educational settings: in the classroom and at home. The study involved 55 middle school students using the AR application to practice basic chemistry concepts. The results suggested that students’ general motivation towards the activity was similar in both settings. However, students who worked at home reported better satisfaction and attention levels compared with those who worked in the classroom. Additionally, students who worked at home made fewer mistakes and achieved better grades compared with those who worked in the classroom. Overall, the study suggests that AR can be exploited as an effective learning environment for learning the basic principles of chemistry in home settings.

Educators seek to develop accurate and timely prediction models to forecast student retention and attrition. Although prior studies have generated single point estimates to quantify predictive efficacy, much less education research has examined variability in student performance predictions using nonparametric bootstrap algorithms in data pipelines. In this study, bootstrapping was applied to examine performance variability among five data mining methods (DMMs) and four filter preprocessing feature selection techniques for forecasting course grades for 3225 students enrolled in an undergraduate biology class. While the median area under the curve (AUC) values obtained from bootstrapping were significantly lower than the AUC point estimates obtained without resampling, DMMs and feature selection techniques impacted variability in different ways. The ensemble technique elastic net regression (GLMNET) significantly outperformed all other DMMs and exhibited the least amount of variability in the AUC. However, all filter feature selection techniques significantly increased variability in student success predictions, compared to when this step was omitted from the data pipeline. We discuss the potential benefits and drawbacks of incorporating bootstrapping into prediction pipelines to track, monitor, and forecast classroom performance, as well as highlight the risks of only examining point estimates.

Augmented reality (AR)-based learning environments are argued to foster cognitive and emotional involvement. Immersion has been identified as one of the driving forces that promote learning in technology-based learning environments. This study evaluated the learning effectiveness and immersion appeal of an AR-marker-based learning activity targeted at practicing basic chemistry concepts. Data were collected from a cohort of 124 middle school students in Mexico and analyzed using pretest–posttest comparisons and cluster analysis. The results suggest that students with higher immersive learning profiles achieve better learning outcomes compared with those with lower immersive profiles.

Author/s Irina Argüelles Álvarez Margarita Martínez Núñez Ana Belén García Hernando Antonio da Silva Fariña Universidad Politécnica de Madrid, Spain ABSTRACT The study presented herein is framed within the Mobile Assisted Language Learning (MALL) approach and presents the results from the use of an original mobile app, developed by the research team, on the part of 73 students. The app prototype consists of multiple choice questions and answers that assess varied aspects of the grammar, vocabulary and use of English at level B2 according to the Common European Framework of Reference for Languages (CEFRL). The application presents motivating gamification components that aim at enhancing students’ participation and regular use of the game. Some of these gamification features are response time, use of competition and training tests, and a score ranking based on nicknames. The study uses different indexes in order to describe the use of the platform and qualitative and quantitative indicators to reach positive conclusions related to students’ increased motivation and improvement in their grammar and vocabulary competence levels.

The rate of technological development across the globe is dramatic. The decreasing cost and increasing availability of ICT devices means that its users are no longer exclusively industry or government employees – they are now also home users. Home users integrate ICT in their daily lives for education, socialising and information gathering. However, using ICT is associated with risks and threats, such as identity theft and phishing scams. Most home users of ICT do not have the necessary information technology and Internet skills to protect themselves and their information. School learners, in particular, are not sufficiently educated on how to use technological devices safely, especially in developing countries such as South Africa. The national school curriculum in South Africa currently does not make provision for cyber-safety education, and the availability of supporting material and training for ICT teachers in South Africa is limited, resulting in a lack of knowledge and skills regarding cyber-safety. The research in hand focuses on the situation concerning cyber-safety awareness in schools and has adopted a short-term approach towards cyber-safety among teachers and school learners in South Africa until a formal long-term national approach has been implemented. This study takes a quantitative approach to investigating the current options of teachers to enhance cyber-safety among learners in their schools. The research proposes that short-term initiatives (i.e. posters) can increase learners’ awareness of cyber-safety until formal cyber-safety awareness methods have been introduced.

Abstract Background Electrochemotherapy is a local treatment that utilizes electric pulses in order to achieve local increase in cytotoxicity of some anticancer drugs. The success of this treatment is highly dependent on parameters such as tissue electrical properties, applied voltages and spatial relations in placement of electrodes that are used to establish a cell-permeabilizing electric field in target tissue. Non-thermal irreversible electroporation techniques for ablation of tissue depend similarly on these parameters. In the treatment planning stage, if oversimplified approximations for evaluation of electric field are used, such as U/d (voltage-to-distance ratio), sufficient field strength may not be reached within the entire target (tumor) area, potentially resulting in treatment failure. Results In order to provide an aid in education of medical personnel performing electrochemotherapy and non-thermal irreversible electroporation for tissue ablation, assist in visualizing the electric field in needle electrode electroporation and the effects of changes in electrode placement, an application has been developed both as a desktop- and a web-based solution. It enables users to position up to twelve electrodes in a plane of adjustable dimensions representing a two-dimensional slice of tissue. By means of manipulation of electrode placement, i.e. repositioning, and the changes in electrical parameters, the users interact with the system and observe the resulting electrical field strength established by the inserted electrodes in real time. The field strength is calculated and visualized online and instantaneously reflects the desired changes, dramatically improving the user friendliness and educational value, especially compared to approaches utilizing general-purpose numerical modeling software, such as finite element modeling packages. Conclusion In this paper we outline the need and offer a solution in medical education in the field of electroporation-based treatments, e.g. primarily electrochemotherapy and non-thermal irreversible tissue ablation. We present the background, the means of implementation and the fully functional application, which is the first of its kind. While the initial feedback from students that have evaluated this application as part of an e-learning course is positive, a formal study is planned to thoroughly evaluate the current version and identify possible future improvements and modifications.