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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]
  • Cover 2

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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: Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Editorial Preface: New Leadership New Era

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      Authors: Minjuan Wang;
      Pages: 434 - 438
      Abstract: Presents the introductory editorial for this issue of the publication.
      PubDate: Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Block-Based Object-Oriented Programming

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      Authors: Oliver Allen;Xavier Downs;Elliot Varoy;Andrew Luxton-Reilly;Nasser Giacaman;
      Pages: 439 - 453
      Abstract: Object-oriented programming (OOP) is not only an integral part of computing degrees but also a requirement in non-computing majors such as engineering. Understanding OOP concepts can be difficult for novice programmers, and often leads to the development of misconceptions. This is exacerbated when the discipline requires students to learn a technical low-level language such as C++, as is the case in many engineering disciplines. We propose a block-based programming language extension, Blockly-OOP, to help students learn core OOP concepts without the technical complexities associated with traditional textual languages. The Blockly-OOP Learning Environment was developed by integrating Blockly-OOP with learning activities that guide students through programming exercises that target popular OOP misconceptions. An evaluation (n = 238) in a second-year programming course (CS2) showed that a block-based programming language helps students improve their understanding of object-oriented concepts, warranting further research in this area.
      PubDate: Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Fostering Balanced Contributions Among Children Through Dialogue
           Visualization

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      Authors: Mehmet Celepkolu;Aisha Chung Galdo;Kristy Elizabeth Boyer;
      Pages: 454 - 466
      Abstract: As children develop conversational skills such as taking turns and openly listening to ideas, they often experience conflicts and inequity within collaborative dialogue for learning. Previous research suggests that increasing children's awareness about their own behaviors during collaboration may help them adjust their behaviors and become better partners. Despite this promise, there are currently no educational technologies designed to support children in visualizing and reflecting on their collaborative dialogues. This article reports on an application that generates interactive visualizations of children's dialogue illustrating their word counts, questions counts and types, dialogue content, keywords from their dialogue, and a video recording of their interaction. We evaluated the application by conducting a study with 20 children who were completing computer science (block-based coding) tasks collaboratively and examined how they changed their dialogues in a subsequent dialogue after interacting with the visualizations of their dialogues. Results show that after viewing their dialogue visualizations, children engaged in more balanced dialogues and that less-engaged students talked more and asked more questions. This article provides evidence that dialogue visualization tools have a great potential for supporting young learners as they deeply think about their own dialogue and improve their collaborative behaviors.
      PubDate: Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • CMKT: Concept Map Driven Knowledge Tracing

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      Authors: Yu Lu;Penghe Chen;Yang Pian;Vincent W. Zheng;
      Pages: 467 - 480
      Abstract: In this article, we advocate for and propose a novel concept map driven knowledge tracing (CMKT) model, which utilizes educational concept map for learner modeling. This article particularly addresses the issue of learner data sparseness caused by the unwillingness to practice and irregular learning behaviors on the learner side. CMKT considers the concept map as a new information source and explicitly exploits its inherent information to help the estimation of the learner's knowledge state. Specifically, the pairwise educational relations in the concept map are formulated as the ordering pairs and are used as mathematical constraints for model construction. The topology information in the concept map is extracted and used as the model input by employing the network embedding techniques. Integrating both educational relation information and topology information in the concept map, CMKT adopts the recurrent neural network to perform knowledge tracing tasks. Comprehensive evaluations conducted on five public educational datasets of four different subjects (more than 8000 learners and their 300 000 records) demonstrate the promise and effectiveness of CMKT: The average area under ROC curve (AUC) and overall prediction accuracy (ACC) achieve 0.82 and 0.75, respectively, and CMKT outperforms all the baselines by at least 12.2% and 9.2% in terms of AUC and ACC.
      PubDate: Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Leveraging Class Balancing Techniques to Alleviate Algorithmic Bias for
           Predictive Tasks in Education

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      Authors: Lele Sha;Mladen Raković;Angel Das;Dragan Gašević;Guanliang Chen;
      Pages: 481 - 492
      Abstract: Predictive modeling is a core technique used in tackling various tasks in learning analytics research, e.g., classifying educational forum posts, predicting learning performance, and identifying at-risk students. When applying a predictive model, it is often treated as the first priority to improve its prediction accuracy as much as possible. Class balancing, which aims to adjust the unbalanced data samples of different class labels before using them as input to train a predictive model, has been widely regarded as a powerful method for boosting prediction accuracy. However, its impact on algorithmic bias remains largely unexplored, i.e., whether the use of class balancing methods would alleviate or amplify the differentiated prediction accuracy received by different groups of students (e.g., female versus male). To fill this gap, our study selected three representative predictive tasks as the testbed, based on which we 1) applied two well known metrics (i.e., hardness bias and distribution bias) to measure data characteristics to which algorithmic bias might be attributed; and 2) investigated the impact of a total of eleven class balancing techniques on prediction fairness. Through extensive analysis and evaluation, we found that class balancing techniques, in general, tended to improve predictive fairness between different groups of students. Furthermore, class balancing techniques (e.g., SMOTE and ADASYN), which add samples to the minority group (i.e., oversampling) can enhance the predictive accuracy of the minority group while not negatively affecting the majority group. Consequently, both fairness and accuracy can be improved by applying these oversampling class balancing methods. All data and code used in this study are publicly accessible via https://github.com/lsha49/FairCBT.
      PubDate: Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Automatic Learning Path Creation Using OER: A Systematic Literature
           Mapping

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      Authors: Anni Siren;Vassilios Tzerpos;
      Pages: 493 - 507
      Abstract: Learning paths are curated sequences of resources organized in a way that a learner has all the prerequisite knowledge needed to achieve their learning goals. In this article, we systematically map the techniques and algorithms that are needed to create such learning paths automatically. We focus on open educational resources (OER), though a similar approach can be used with other types of learning objects. Our method of mapping goes through three passes of selected literature. First, we selected all articles mentioning OER and machine learning from IEEE, SCOPUS, and ACM. This resulted in a set of 347 papers after removing duplicates. Of these, 13 were selected as relating to learning paths and their references and citations were identified and organized into eight categories identified in this article (metadata, linked data, recommendation systems, concept maps, knowledge graphs, classification, and learning paths). After identifying these topics, a manual review was conducted resulting in the final set of 112 papers. This article combines the found categories into three steps for learning path creation, which are then discussed in detail. These steps are as follows: 1) concept extraction; 2) relationship mapping; and 3) path creation. Current research relates primarily to enhancing concept extraction and relationship mapping. We identify directions for potential future research that focus on automatically augmenting previously created learning paths in accordance with the changing needs of learners.
      PubDate: Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Tools Designed to Support Self-Regulated Learning in Online Learning
           Environments: A Systematic Review

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      Authors: Ronald Pérez Álvarez;Ioana Jivet;Mar Pérez-Sanagustín;Maren Scheffel;Katrien Verbert;
      Pages: 508 - 522
      Abstract: Self-regulated learning (SRL) is a crucial higher-order skill required by learners of the 21st century, who will need to become lifelong learners to adapt to the continually changing environments. Literature provides examples of tools for scaffolding SRL in online environments. In this article, we provide the state-of-the-art concerning tools that support SRL in terms of theoretical models underpinning development, supported SRL processes, tool functionalities, used data and visualizations. We reviewed 42 articles published between 2008 and 2020, including information from 25 tools designed to support SRL. Our findings indicate that: 1) many of the studies do not explicitly specify the SRL theoretical model used to guide the design process of the tool; 2) goal setting, monitoring, and self-evaluation are the most prevalent SRL processes supported through functionalities, such as content navigation, user input forms, collaboration features, and recommendations; 3) the relationship between tool functionalities and SRL processes are rarely described; and 4) few tools assess the impact on learners’ SRL process and learning performance. Finally, we highlight some lessons learned that might contribute to implementing future tools that support learners’ SRL processes.
      PubDate: Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
 
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