Smart Learning Environments最新文献

The primary question of this study is whether OLM and OSLM mechanisms, when used in a digital game, offer higher motivation. Furthermore, the study investigates whether a game's aesthetics and mechanics support players' intrinsic motivation. Both claims are tested through the design, implementation and pilot use of the Multiplication Game (MG). MG is a digital learning activity that supports pupils in achieving multiplication competence and provides teacher a dashboard to assess and watch own pupils' performance. The game enriched with gamification elements to engage and motivate participants. Three versions of the game were used by pupils: without any Open Learner Modeling (OLM) support (i.e. without providing access to own progress data), with OLM support and with Open Social Learner Modeling (OSLM) support, to investigate the difference in motivation among these characteristics. After using the MG for a 2-month period, pupils answered a questionnaire anonymously to express their opinion about MG mechanics, MG aesthetics and intrinsic motivation MG can offer. Furthermore, the corresponding teachers were interviewed to provide insights on their attitude towards MG and its functionalities. A statistically significant difference in Intrinsic Motivation (IM) between the three different MG versions was found and a statistically significant difference in MG Aesthetics and Mechanics between the different grades of primary school. Additionally, Intrinsic Motivation was positively correlated with gamification motivators and MG Aesthetics. Participating teachers stated that MG can improve pupils' multiplication competence and it is worthy of a stable place in the instructional procedure, as it is a means of pupils' progress tracking and (self-) assessment, as well as a fun way of practicing and developing multiplication skills.

The tremendous growth in electronic educational data creates the need to have meaningful information extracted from it. Educational Data Mining (EDM) is an exciting research area that can reveal valuable knowledge from educational databases. This knowledge can be used for many purposes, including identifying dropouts or weak students who need special attention and discovering extraordinary students who can be offered lifetime opportunities. Although former studies in EDM used an extensive range of features for predicting students' academic achievement (in terms of (i) achieved grades or (ii) passing and failing), those features are sometimes not obtainable for practical usage, and therefore, the prediction models are not feasible for employment. This study uses data mining (DM) algorithms to predict the academic performance of master' s students by using a non-extensive data set and including only the features that are easy to collect at the beginning of a studying program. To perform this study, we have collected over 700 students' records from 2010 to 2018 from the Faculty of Business Informatics and Mathematics at the University of Mannheim in Germany. Those records include demographics and post-enrollment features such as semester grades. The empirical results show the following: (i) the most significant features for predicting students' academic achievements are the students' grades in each semester (importance rate between 14 and 36%), followed by the distance from students' accommodation to university (importance rate between 6 and 18%) and culture (importance rate between 7 and 17%). On the other hand, gender, age, the numbers of failed courses, and the number of registered and unregistered exams per semester are less significant for the predictions. (ii) As expected, predictions performed after the second semester is more accurate than those performed after the first semester. (iii) Unsurprisingly, models that predict two classes yield better results than those that predict three. (iv) Random Forest classifier performs the best in all prediction models (0.77-0.94 accuracy), and using oversampling methods to deal with imbalanced data can significantly improve the performance of DM methods. For future work, we recommend testing the predictive models on other master programs and a larger datasets. Furthermore, we recommend investigating other oversampling approaches.

Technology has enabled the higher education ecosystem to tailor to the students who have diverse needs and to engage with them remotely, especially when face-to-face interaction is not possible. This research contributes knowledge in forced remote learning during the unprecedented global pandemic situation of Covid-19. Using a cross-sectional quantitative method, a total of 480 respondents among undergraduate students from five private universities in Malaysia participated in this study. The data was analysed using structural equation modelling and results indicated that online feedback, online future relevance, online interaction, online teaching effectiveness, and personal well-being were statistically significant in influencing students' satisfaction. Moreover, online learning satisfaction directly predicted 68.3% of the students' continuous usage intention while their usage intention was heightened with higher levels of proficiency in online learning. Students' satisfaction was found to be a significant mediator between all the factors towards usage intention except online assessment, online support, and personal well-being. This study provides the higher education institutions with insights to continuously improve their online delivery strategies and bridge the gap with their students during the pandemic crisis.

The education system evolves and transforms towards interactive and immersive learning tools in this digital age. Augmented reality has also evolved as a ubiquitous, robust, and effective technology for providing innovative educational tools. In engineering education, many abstract concepts require technological intervention for conceptual understanding and better instructional content. While learning through the immersive tools, system usability has great importance in terms of effectiveness, efficiency, and satisfaction. Effectiveness refers to users' accuracy and completeness in achieving defined goals; efficiency relates to expended resources about the precision and completeness with which users achieve their objectives; satisfaction deals with a positive attitude towards using the product. If the system fails to provide good usability, it may cause adverse effects such as increasing stress, lacking necessary features, increasing the users' cognitive load, and negatively impacting the student's motivation. In this study, two mobile augmented reality (MAR) applications were developed as an instructional tool to teach the students about Karnaugh maps in the digital electronics course. The first application is a Keypad-based MAR application that uses a keypad matrix for user interaction and the second application is a Marker-based MAR application that uses multiple markers to solve K-Map for producing an optimum solution of the given problem. An experimental study was conducted to determine the student's opinion of the developed MAR applications. The study was designed to determine the system usability of the two MAR applications using the System Usability Score (SUS) and Handheld Augmented Reality Usability Score (HARUS) models. 90 engineering students participated in the study, and they were randomly divided into two different groups: keypad-based group and Marker-based group. The keypad-based group included 47 students who had hands-on experience with a keypad-based MAR application, whereas the marker-based group included 43 students who had hands-on experience with multiple marker-based MAR applications. The experimental outcomes indicated that the keypad-based MAR application has better SUS and HARUS scores than the marker-based MAR application which suggests that the keypad-based MAR application has provided better user interaction.

The COVID-19 pandemic caused many educational institutions in the world to switch to the distance education process, and this process was called "Emergency Remote Teaching". This urgent transition process has caused many problems in educational environments. One of the problems is the subject of measurement and evaluation. Along with the pandemic, many institutions have used various online assessment systems to make measurements and evaluations online, and researchers have conducted research on these online assessment systems. This research focus on the features of the online assessment systems and aims to examine the trends towards the prominent features of the online assessment systems in the Emergency Remote Teaching period. For this purpose, the prominent online assessment systems have been determined by systematically analyzing academic studies published in 2020, and answers have been sought to the following research questions: (1) which platforms they support, (2) which security features they have, and (3) what common features they have. Identifying trends in the characteristics of online assessment systems is expected to guide practitioners, decision-makers, researchers, and system developers in the process of selecting and/or developing an online assessment system for use in online measurement and evaluation.

There are many examples of hybrid simulation models in healthcare education which are designed to simulate specific scenarios. However, there appears to be a need for a cost effective and configurable hybrid simulation platform which can be used by educators of various healthcare disciplines to simulate different scenarios. The purpose of this paper is to develop a proof-of-concept platform that can be easily implemented at little cost and provide flexibility to healthcare instructors to develop a variety of simulation scenarios, and to determine the effectiveness of this platform. Using a standardized patient, a person acting as a patient in a scripted manner, along with wearable and web-based technologies, a congestive heart failure simulation was used as an evaluative exercise for a group of personal support worker students at a Canadian Community College. Personal support workers typically provide care to any person who may require personal assistance with activities of daily living such as feeding, lifting, bathing, skin care and oral hygiene to name a few. Standardized patients are typically used in healthcare education to educate and evaluate soft skills, such as caregiver to patient communication, professionalism, as well as hard skills, such as history taking, examination and diagnostic skills (Rosen in J Crit Care 23:157-166, 2008). Instructor feedback indicated that the platform was easy to use and capable of simulating a large variety of scenarios. Pre and post test results are evidence of initial findings of promise indicating that the platform seemed to be effective in enabling students to meet learning outcomes. Focus group results seem to indicate an increase in student confidence as it relates to their ability to handle a similar scenario in the workplace.

Technology adoption for school education further gained momentum during the COVID-19 pandemic. However, the challenges and strategies of children belonging to the less privileged (we use 'privileged' in the article to identify those enjoying a standard of living or rights as majority of people in the society) families are different from those of the children who come from socio-economically better-off (privileged) backgrounds. The purpose of this research is to explore the experiences of children with school education and using technology for learning. Past studies have highlighted the use of internet and communication technologies as a promising solution to provide quality school education in the remotest parts of the country. Previous research has also ascertained that the socio-economic status divide has no significant impact on the students' ability to learn using technology. Children can use technology to learn irrespective of their socio-economic status and background. We conducted this exploratory qualitative study from a constructivist grounded theory perspective. A purposive sample of 14 students (9 from underprivileged and 5 from privileged family backgrounds) in the age group of 6-14 years, was used and unstructured interviews were conducted. We analysed the data using constructivist grounded theory methodology. We found that the experiences of privileged and underprivileged children differed with respect to access to internet, affordability of ICT device, quality teachers, parental support, and financial sponsorship. However, the experiences and perspectives of the children were found to be similar with respect to personal ownership of mobile phone device for unlimited time at own disposal, self-directed learning and having a trusted study advisor. The findings may be useful to policy makers and EdTech firms to build strategies and solutions for effective implementation of universal school education in the country.

The purpose of this study is to discover the main themes and categories of the research studies regarding digital literacy. To serve this purpose, the databases of WoS/Clarivate Analytics, Proquest Central, Emerald Management Journals, Jstor Business College Collections and Scopus/Elsevier were searched with four keyword-combinations and final forty-three articles were included in the dataset. The researchers applied a systematic literature review method to the dataset. The preliminary findings demonstrated that there is a growing prevalence of digital literacy articles starting from the year 2013. The dominant research methodology of the reviewed articles is qualitative. The four major themes revealed from the qualitative content analysis are: digital literacy, digital competencies, digital skills and digital thinking. Under each theme, the categories and their frequencies are analysed. Recommendations for further research and for real life implementations are generated.

Background: Recognizing learners' engagement during learning processes is important for providing personalized pedagogical support and preventing dropouts. As learning processes shift from traditional offline classrooms to distance learning, methods for automatically identifying engagement levels should be developed.

Objective: This article aims to present a literature review of recent developments in automatic engagement estimation, including engagement definitions, datasets, and machine learning-based methods for automation estimation. The information, figures, and tables presented in this review aim at providing new researchers with insight on automatic engagement estimation to enhance smart learning with automatic engagement recognition methods.

Methods: A literature search was carried out using Scopus, Mendeley references, the IEEE Xplore digital library, and ScienceDirect following the four phases of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA): identification, screening, eligibility, and inclusion. The selected studies included research articles published between 2010 and 2022 that focused on three research questions (RQs) related to the engagement definitions, datasets, and methods used in the literature. The article selection excluded books, magazines, news articles, and posters.

Results: Forty-seven articles were selected to address the RQs and discuss engagement definitions, datasets, and methods. First, we introduce a clear taxonomy that defines engagement according to different types and the components used to measure it. Guided by this taxonomy, we reviewed the engagement types defined in the selected articles, with emotional engagement (n = 40; 65.57%) measured by affective cues appearing most often (n = 38; 57.58%). Then, we reviewed engagement and engagement-related datasets in the literature, with most studies assessing engagement with external observations (n = 20; 43.48%) and self-reported measures (n = 9; 19.57%). Finally, we summarized machine learning (ML)-based methods, including deep learning, used in the literature.

Conclusions: This review examines engagement definitions, datasets and ML-based methods from forty-seven selected articles. A taxonomy and three tables are presented to address three RQs and provide researchers in this field with guidance on enhancing smart learning with automatic engagement recognition. However, several key challenges remain, including cognitive and personalized engagement and ML issues that may affect real-world implementations.