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  Subjects -> ELECTRONICS (Total: 207 journals)
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IEEE Transactions on Learning Technologies
Journal Prestige (SJR): 0.783
Citation Impact (citeScore): 3
Number of Followers: 12  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 1939-1382 - ISSN (Online) 1939-1382
Published by IEEE Homepage  [228 journals]
  • IEEE Transactions on Learning Technologies

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Past, Present, and Future of TLT's Journey in Publishing

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      Authors: Minjuan Wang;
      Pages: 650 - 655
      Abstract: Presents the editorial for this issue of the publication.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • A Bibliometric Overview of the IEEE Transactions on Learning Technologies

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      Authors: Gustavo Zurita;Carles Mulet-Forteza;José M. Merigó;Valeria Lobos-Ossandón;Hiroaki Ogata;
      Pages: 656 - 672
      Abstract: IEEE Transactions on Learning Technologies (IEEE-TLT) is a leading journal in the fields of computer science and educational research with a focus on learning technologies. It published its first issue in 2008 and commemorated its 15th anniversary in 2022. Inspired by this event, this article provides a general lifetime overview of the journal using bibliometric indicators and science mapping analysis. The main objective is to provide a complete overview of the main components that have affected the journal. This analysis includes key factors such as the most cited articles and the leading authors, institutions, and countries for the journal, along with an insight into the publication and the citation structure. We use the Web of Science Core Collection database to analyze the bibliometric data and VOSviewer software to graphically map the bibliographic material using a bibliographic coupling, cocitation, and co-occurrence of author keywords. With this analysis, we gain a deeper understanding of how IEEE-TLT is connected to other journals and researchers across the globe and how it contributes to scientific communities. Results indicate that IEEE-TLT is a high-impact journal in computer Science and education and has been referenced by a wide range of authors, institutions, countries, and the main topics related to learning technologies from all over the world.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • An Integrated Framework to Support Bachelor Design Students in 3-D
           Modeling

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      Authors: Andrea Sanna;Federico Manuri;Francesco De Pace;Fabrizio Valpreda;Claudio Fornaro;
      Pages: 673 - 684
      Abstract: Design and computer graphics curricula for tertiary education include 3-D modeling skills. Students must learn to represent (complex) 3-D objects by means of parametric surfaces or polygonal meshes. Three-dimensional modeling may be a complex task, and students must be able to accrue a certain deal of experience in the field before passing the exam. The main difficulties the students must face mainly concern the comprehension of 3-D shapes and the choice of the most appropriate modeling techniques. This article proposes a framework to support bachelor's degree students in modeling 3-D objects by means of parametric surfaces. The proposed framework provides two augmented reality (AR) apps for smartphones and a web portal. The mobile AR apps allow students to deeply visualize 3-D object shapes, thus performing a set of guided exercises to practice basic modeling techniques. On the other hand, the web portal allows students to share their models with teachers and classmates to obtain feedback and comments. The preliminary results show the effectiveness of the proposed solution, as all volunteers involved in the experimental phase achieved better results after using the tools. Moreover, the students' opinions about the proposed framework are positive.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Constructing an Edu-Metaverse Ecosystem: A New and Innovative Framework

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      Authors: Minjuan Wang;Haiyang Yu;Zerla Bell;Xiaoyan Chu;
      Pages: 685 - 696
      Abstract: The Metaverse is a network of 3-D virtual worlds supporting social connections among its users and enabling them to participate in activities mimicking real life. It merges physical and virtual reality and provides channels for multisensory interactions and immersions in a variety of environments (Mystakidis, 2022). The Metaverse is considered the third wave of the Internet revolution, and it is built on new and emerging technologies such as extended reality and artificial intelligence. Research on the impact of the Metaverse on education exploded in 2022. Here, we explore learning across the Metaverse and propose a new and innovative theoretical framework by reviewing literature and synthesizing best practices in designing metaverse learning environments. This ecosystem consists of four major hubs: 1) instructional design and performance technology hub; 2) knowledge hub; 3) research and technology hub; and 4) talent and training hub. Common to all four hubs are the factors in the three wheels: 1) infrastructure, business industry, and communication; 2) technology access and equity; and 3) user rights, data security, and privacy policy. We believe that this framework can help guide emerging research and development on the applications of the Metaverse in education. We also hope this article can serve as a launch pad for the special issue on the Metaverse and the Future of Education supported by the IEEE Education Society.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Developing an E-Learning Platform Capable of Being Aware of Self-Regulated
           Learning Behaviors of Role Models

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      Authors: Jian-Wei Lin;Hao-Chiang Koong Lin;Hong-Ren Chen;
      Pages: 697 - 708
      Abstract: Conventional e-learning platforms require a high self-regulatory learning (SRL) ability to ensure learning effectiveness. However, because not everyone has high autonomy and a high SRL ability, many students quit during the online learning period. To enhance the SRL ability, many studies have developed e-learning platforms based on Zimmerman's SRL training model. However, these platforms still require learners to complete a series of provided learning tasks independently and autonomously. This article developed an e-learning platform based on Zimmerman's SRL training model by using role-model group awareness tools to visualize the learning context and activities of the group of role models in each SRL training phase for learners’ observation, self-reflection, and emulation. To test the model, the learning outcomes of the two groups of students enrolled in the same course were measured experimentally. The experimental class consisted of 36 students using the proposed system, whereas the control class consisted of 35 students using a traditional e-learning platform. The experimental class spent more learning time on the platform and achieved better learning results than did the control class. Moreover, the experimental class had gradually improved assessment scores and SRL-related behaviors. The final section presents explanations, discussion, and implications derived from analytical results.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Educational Process Mining for Discovering Students'
           Problem-Solving Ability in Computer Programming Education

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      Authors: Fang Liu;Liang Zhao;Jiayi Zhao;Qin Dai;Chunlong Fan;Jun Shen;
      Pages: 709 - 719
      Abstract: Educational process mining is now a promising method to provide decision-support information for the teaching–learning process via finding useful educational guidance from the event logs recorded in the learning management system. Existing studies mainly focus on mining students' problem-solving skills or behavior patterns and intervening in students' learning processes according to this information in the late course. However, educators often expect to improve the learning outcome in a proactive manner through dynamically designing instructional strategies prior to a course that are more appropriate to students' average ability. Therefore, in this article, we propose a two-stage problem-solving ability modeling approach to obtain students' ability in different learning stages, including the pre-problem-solving ability model and the post-problem-solving ability model. The models are trained with Gradient Boosting Decision Tree (GBDT) on the historical event logs of the prerequisite course and the target course, respectively. With the premodel, we establish the students' pre-problem-solving ability profiles that reflect their average knowledge level before starting a course. Then, the instructional design is dynamically chosen according to the profiles. After a course completes, the post-problem-solving ability profiles are generated by the postmodel to analyze the learning outcome and prompt the learning feedback, in order to complete the closed-loop teaching process. We study the modeling of coding ability in computer programming education to show our teaching strategy. The experimental results show that the generalizable problem-solving ability models yield high classification precision, while most students' abilities have been significantly improved by the proposed approach at the end of the course.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Educational Sequence Mining for Dropout Prediction in MOOCs: Model
           Building, Evaluation, and Benchmarking

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      Authors: Galina Deeva;Johannes De Smedt;Jochen De Weerdt;
      Pages: 720 - 735
      Abstract: Due to the unprecedented growth in available data collected by e-learning platforms, including platforms used by massive open online course (MOOC) providers, important opportunities arise to structurally use these data for decision making and improvement of the educational offering. Student retention is a strategic task that can be supported by means of automated data-driven dropout prediction. Given the time-based nature of the collected data (user activity), these data can be viewed as sequences, and thus, sequence mining presents itself as a fitting set of techniques to automatically extract valuable insights. However, there is a lack of general guidelines for using sequence mining in specific educational settings, as well as little information on how different techniques perform in comparison to each other. We address these limitations with two main contributions. First, we propose a framework for applying sequence classification for dropout prediction in MOOCs. This framework includes two data-driven dropout definitions, the specification of data formatting and preparation tasks, and a blackprint on how to train dropout prediction models at suitable time points in the run of the course. Second, we conduct a benchmarking study of recent and well-performing sequence classification techniques, tested with different parametrizations on 47 real-life datasets from MOOCs, resulting in a comparative assessment of over 18 000 models. Our results provide insight into the performance differences between the techniques and allow us to formulate concrete recommendations toward the choice of suitable hyperparameters that have a significant influence on the predictive performance.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Evaluating the Effectiveness of Accommodations Given to Students With
           Learning Impairments: Ordinal and Interpretable Machine-Learning-Based
           Methodology

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      Authors: Gonen Singer;Maya Golan;Rachel Shiff;Dvir Kleper;
      Pages: 736 - 746
      Abstract: In most academic institutions, students with learning impairments (LIs) are entitled to various accommodations as a means of compensating for their impairment. Ensuring that the appropriate accommodations were selected requires an intelligent support tool to track their effectiveness. In this article, we regard the effectiveness of such accommodations in terms of their quality and reliability. High-quality accommodations allow students with LIs equal access and equal opportunity to demonstrate their knowledge compared to their peers who do not have LIs. Highly reliable accommodations mean a significant performance difference between students with LIs who actually use the accommodations compared with students with LIs who do not use the accommodations, given similar student characteristics. Previous literature is inconclusive regarding the evidence of the effectiveness of such accommodations since different accommodations may have a different effect on different subgroups of exams and students. This article proposes a methodology, based on ordinal interpretable models, that produce practical insights to professionals who are responsible for students with LIs, to address the problem of exploring the effectiveness of learning accommodations. The suggested models use the ordinal information of the target variables for evaluating student performance and yield practical insights for designing the most suitable accommodation for a student with LIs based on their characteristics. The ordinal interpretable models are evaluated using a database of tens of thousands of engineering students. The results demonstrate that the suggested interpretable models perform significantly better than the compared algorithms on all measures.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Extracting the Relationships Among Students Based on Accessing Pattern of
           Digital Learning Attributes

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      Authors: Samya Muhuri;Debajyoti Mukhopadhyay;
      Pages: 747 - 756
      Abstract: A paradigm shift can be expected in the education sector, especially after the COVID-19 pandemic. E-learning systems are being adopted by all the stakeholders as physical meetings are not feasible. Different online learning attributes, such as video conferencing tools, coding platforms, online learning frameworks, digital books, and online videos, are available, which are enhancing the traditional learning methodology. Now, the main challenge for the educationists is to identify how these attributes are utilized by the learners. In this article, we have represented any online class as a social network where students are connected through learning platforms. We have mined the network based on several network measuring parameters to recognize the maneuvering pattern of these digital resources or attributes by the students. We have also proposed a community detection method that would form different groups among the students based on their comfortable learning patterns. As a case study, we have scrutinized the accessing patterns of different digital learning resources by some particular students. The experimental results show the significant relationship between the digital resource accessing patterns of the students with their immediate performance in the test. The satisfactory inquisitive results of our approach would definitely inspire the researchers of interdisciplinary areas to probe further in this domain.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning

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      Authors: Maria Kalantzi;Agoritsa Polyzou;George Karypis;
      Pages: 757 - 770
      Abstract: Automated team formation is becoming increasingly important for a plethora of applications in open-source community projects, remote working platforms, as well as online educational systems. The latter case, in particular, poses significant challenges that are specific to the educational domain. Indeed, teaming students aims to accomplish far more than the successful completion of a specific task. It needs to ensure that all the members in the team benefit from the collaborative work, while also ensuring that the participants are not discriminated against with respect to their protected attributes, such as race and gender. Toward achieving these goals, this article introduces FERN, a fair team formation approach that promotes mutually beneficial peer learning, dictated by protected group fairness as equality of opportunity in collaborative learning. We formulate the problem as a multi-objective discrete optimization problem. We show this problem to be NP-hard and propose a heuristic hill-climbing algorithm. Extensive experiments on both the synthetic and real-world datasets against well-known team formation techniques show the effectiveness of the proposed method.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Learning an Alternative Car-Following Technique to Avoid Congestion With
           an Instructional Driving Simulator

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      Authors: Antonio Lucas-Alba;Sharona T. Levy;Óscar M. Melchor;Ana Zarzoso-Robles;Ana M. Ferruz;Maria Teresa Blanch;Andrés S. Lombas;
      Pages: 771 - 782
      Abstract: This article addresses the problem of traffic congestion through a learning perspective, highlighting the capabilities of information, and communication technologies to transform society. Recent physical and mathematical analysis of congestion reveals that training drivers to keep a safe distance systematically contributes to the emergence and maintenance of interference congestion (so-called phantom traffic jam). This article presents the WaveDriving Course (WDC), a simulated learning environment designed to help drivers progress from the traditional drive-to-keep-distance (DD) technique to a new car-following (CF) principle better suited for wave-like traffic, drive-to-keep-inertia (DI). The WDC is based on the ordinary knowledge of the driver (e.g., going through a series of traffic lights) and presents this situation in terms of two possible simultaneous behavioral strategies. The driver has the opportunity to verify that it is possible to achieve the same objective with different consequences. Finally, the WDC checks to what extent this learning generates transfer patterns in the analogous case of CF. This article focuses on results concerning the first WDC module: the traffic-light analogy. Forty-two participants followed the whole learning procedure for about 30 min. An evaluative CF test was administered before and after visioning the tutorial and practicing on the simulator. Overall, transference from this traffic-light analog to the CF situation (posttest) was successful. Results confirm the adoption of the expected DI strategies (speed variability decreased, distance and distance variability to leader increased, fuel consumption decreased, platoon elongation decreased, etc.). The need to improve the WDC teaching of the appropriate CF distance is discussed.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Mining Sequential Learning Trajectories With Hidden Markov Models For
           Early Prediction of At-Risk Students in E-Learning Environments

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      Authors: Anika Gupta;Deepak Garg;Parteek Kumar;
      Pages: 783 - 797
      Abstract: With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the students learning behavior. This further helps them in data-driven decision making through timely intervention via early warning systems (EWS), reflecting and optimizing educational environments, and refining pedagogical designs. In this, the role of EWS is to timely identify the at-risk students. This study proposes a modeling methodology deploying interpretable Hidden Markov Model for mining of the sequential learning behavior built upon derived performance features from light-weight assessments. The public OULA dataset having diversified courses and 32 593 student records is used for validation. The results on the unseen test data achieve a classification accuracy ranging from 87.67% to 94.83% and AUC from 0.927 to 0.989, and outperforms other baseline models. For implementation of EWS, the study also predicts the optimal time-period, during the first and second quarter of the course with sufficient number of light-weight assessments in place. With the outcomes, this study tries to establish an efficient generalized modeling framework that may lead the higher educational institutes toward sustainable development.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Multiuser Digital Platform to Promote Interaction Skill in Individuals
           With Autism

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      Authors: Pradeep Raj Krishnappa Babu;Sujata Sinha;Arvind S. Roshaan;Uttama Lahiri;
      Pages: 798 - 811
      Abstract: Individuals with autism spectrum disorder (ASD) face milestones in understanding other's preferences and intentions that in turn affect their reciprocity and interaction skills during a collaborative partnership. Investigators are advocating the use of digital-medium-based multiuser platforms to encourage learning of collaborative interaction skills among these children. Although currently available platforms encourage interaction, these do not consider aspects, e.g., anticipating and understanding the partner's preference (or intentions) along with spontaneous reciprocation through turn-taking, important for nurturing effective interaction. In this article, we have developed a multiuser virtual-reality-based interaction skill learning platform (M-VISP) in which the users can interact with each other through turn-taking using the digital platform. Successful execution of the task (quantified in terms of performance) needs them to understand each other's preferences before reciprocating. A usability study was designed in which individuals with ASD (n = 18) and typical development (n = 18) volunteered. Results indicated that the multiuser interaction facility offered by M-VISP could quantify one's ability to understand the preference of a partner in terms of their performance in a task and the spontaneity with which one reciprocated. Additionally, we observed an improving trend in the ability to understand a partner's preference along with exhibiting spontaneity in reciprocation among individuals with ASD.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Pilot Study Using Decision Trees to Diagnose the Efficacy of Virtual
           Offshore Egress Training

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      Authors: Jennifer Smith;Mashrura Musharraf;Brian Veitch;Faisal Khan;
      Pages: 812 - 826
      Abstract: For the offshore energy industry, virtual environment technology can enhance conventional training by teaching basic offshore safety protocols such as onboard familiarization and emergency evacuation. Virtual environments have the added benefit of being used to investigate the impact of different training approaches on competence. This pilot study uses decision tree modeling to examine the efficacy of two pedagogical approaches, simulation-based mastery learning (SBML) and lecture-based training (LBT), in a virtual environment. Decision trees are an inductive reasoning approach that can be used to identify learners’ egress strategies in offshore emergencies after training. The efficacy of the virtual training is evaluated in three ways: 1) analyzing participants’ performance scores in test scenarios; 2) comparing the decision tree depiction of participant's understanding of emergency egress to the intended learning objectives; and 3) comparing the decision strategies developed under a different pedagogical approach. A comparison of the resulting decision trees from the SBML training with trees generated from the LBT showed that the different training methods influenced the participants’ egress strategies. The SBML approach resulted in concise decision trees and better route selection strategies when compared to the LBT training. This pilot study demonstrates the diagnostic capabilities of decision trees as training assessment tools and recommends integrating decision trees into virtual training to better support the learning needs of individuals and deliver adaptive training scenarios.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Serious Game Design in Health Education: A Systematic Review

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      Authors: Allan Amaral Tori;Romero Tori;Fátima de Lourdes dos Santos Nunes;
      Pages: 827 - 846
      Abstract: Inclusion of game elements in health education has proved to be effective in helping student training. Commonly termed as “serious games” these gamified systems can be an alternative to empower and motivate students during the learning process. The literature contains serious games for professional training in many health-related areas, including several motivating and playful gamification elements, and a variety of evaluation techniques used. Some review studies have compiled articles that present serious games for health-related areas analyzing aspects such as development methodologies and assessment techniques. However, the playful aspects that contribute to the health education process have not yet been compiled. This article focuses on a systematic review that analyzes the state of the art regarding serious games for health-related education, and evaluates the following: 1) game elements; 2) platforms, evaluation methods; and 3) requirements analysis methods. The findings indicate that “Tasks,” “Score,” and “Level Progression” were the most used gamification elements. Physiotherapy, psychology, and physical education were the areas most covered by the included articles. Pre and posttest questionnaires were identified as the main methods used to evaluate the serious games. The article contributes with an overview of the serious games design process, abstracted from the performed review and depicted in a diagram showing the phases commonly found in our study. The article also proposes a categorization for the most used game elements and evaluation methods.
      PubDate: Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
 
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