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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: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Design and Application of Automatic Feedback Scaffolding in Forums to
           Promote Learning

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      Authors: Qi Wang;Carolyn P. Rose;Ning Ma;Shiyan Jiang;Haogang Bao;Yanyan Li;
      Pages: 150 - 166
      Abstract: Forums are essential components facilitating interactions in online courses. However, in large-scale courses, many posts generated, which results in learners’ difficulties. First, the posts are poorly organized and some deviate from the topic, making it difficult for learners’ knowledge acquisition. Second, learners cannot receive timely feedback and guidance, making the learning progress unclear for them. Well-designed scaffoldings should be built based on challenges of forums to improve learners’ learning outcomes, knowledge construction, and completion rate. While targeting the problems in online forums, this article proposed principles for the design of online scaffolding after analyzing the requirements of online learning scaffolding or scripts. Subsequently, in this article, we designed an automatic feedback scaffolding based on the principles and a knowledge construction model. The scaffolding provided learners with timely feedback and related learning guidance. Tags were used to assist learners in acquiring relevant information more easily. The scaffolding was then integrated into the Learning Cell Knowledge Community and used in an online course for 955 learners. The results showed that automatic feedback scaffolding positively affected learners’ learning and promoted positive knowledge transformation. Furthermore, we found that the scaffolding could help learners induce more constructive behaviors defined in the Interactive, Constructive, Active, and Passive deep learning framework that demonstrated the reason for learners’ knowledge transformation. At last, learners’ course completion rate also increased with the help of the scaffolding, which provided evidence that well-designed scaffolding can result in positive educational outcomes. In addition, the principles proposed could also contribute to further scaffolding design and practices.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • A Petri-Net-Based Approach for Enhancing Clinical Reasoning in Medical
           Education

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      Authors: Fabrizio L. Ricci;Fabrizio Consorti;Fabrizio Pecoraro;Daniela Luzi;Oscar Tamburis;
      Pages: 167 - 178
      Abstract: Medical students are called to acquire competence to manage disease in its dynamic evolution over time, learning to analyze how clinical conditions evolve in a patient's history and how each condition interferes with the evolution of the other coexisting conditions. In this article, the health issue network (HIN) approach is introduced as a formal language based on Petri nets (PNs) to model properties that are particularly apposite for the graphical representation of HIN evolutionary paths. Moreover, the PNs’ underlying mathematical model allows users to draw coherent and well-formed graphs representing rather complex clinical cases. Finally, HIN can be easily integrated into a simulation environment to support case-based learning activities and assessment. The examples of the exercises provided in this article show, on the one hand, the ways the introduced methodology is figured out and implemented; on the other hand, they outline the variety of learning questions that users may deal with when deploying the HIN approach.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Toward Automatic Interpretation of Narrative Feedback in Competency-Based
           Portfolios

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      Authors: Joyce M. W. Moonen-van Loon;Marjan Govaerts;Jeroen Donkers;Peter van Rosmalen;
      Pages: 179 - 189
      Abstract: Self-directed learning is generally considered a key competence in higher education. To enable self-directed learning, assessment practices increasingly embrace assessment for learning rather than the assessment of learning, shifting the focus from grades and scores to provision of rich, narrative, and personalized feedback. Students are expected to collect, interpret, and give meaning to this feedback, in order to self-assess their progress and to formulate new, appropriate learning goals and strategies. However, interpretation of aggregated, longitudinal narrative feedback has been proven to be very challenging, cognitively demanding, and time consuming. In this article, we, therefore, explored the applicability of existing, proven text mining techniques to support feedback interpretation. More specifically, we investigated whether it is possible to automatically generate meaningful information about prevailing topics and the emotional load of feedback provided in medical students’ competence-based portfolios (N = 1500), taking into account the competence framework and the students’ various performance levels. Our findings indicate that the text-mining techniques topic modeling and sentiment analysis make it feasible to automatically unveil the two principal aspects of narrative feedback, namely the most relevant topics in the feedback and their sentiment. This article, therefore, takes a valuable first step toward the automatic, online support of students, who are tasked with meaningful interpretation of complex narrative data in their portfolio as they develop into self-directed life-long learners.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Modeling of Online Learners’ Sentiments About Multigranularity
           Knowledge

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      Authors: Anping Zhao;Yu Yu;
      Pages: 190 - 198
      Abstract: To provide insight into online learners’ interests in various knowledge from course discussion texts, modeling learners’ sentiments and interests at different granularities are of great importance. In this article, the proposed framework combines a deep convolutional neural network and a hierarchical topic model to discover the hidden structure of online learners’ sentiments about knowledge topics. The approach is to capture multigranularity knowledge of topics of interest to learners with the hierarchical topic model and to identify information about learners’ different sentiments with the convolutional neural network. This approach not only models knowledge of hierarchical interest from general to specific but also identifies learners and their sentiment orientations to better correspond to the different granularities of knowledge. The experimental results and analysis of real-world datasets show that the proposed approach is effective and feasible.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Automating Gamification Personalization to the User and Beyond

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      Authors: Luiz Rodrigues;Armando M. Toda;Wilk Oliveira;Paula Toledo Palomino;Julita Vassileva;Seiji Isotani;
      Pages: 199 - 212
      Abstract: Personalized gamification explores user models to tailor gamification designs to mitigate cases wherein the one-size-fits-all approach ineffectively improves learning outcomes. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several combinations to tailor. Consequently, tools for automating gamification personalization are needed. However, which of those characteristics are relevant and how to do such tailoring are open questions. Furthermore, the required automating tools are lacking. We tackled these problems in two steps. First, we conducted an exploratory study, collecting participants' opinions on the game elements they consider the most useful for different learning activity types (LAT) via survey. Then, we modeled opinions through Conditional Decision Trees to address the aforementioned tailoring process. Second, as a product of the first step, we implemented a recommender system that suggests personalized gamification designs (which game elements to use), addressing the problem of automating gamification personalization. Our findings present empirical evidence that LAT, geographic locations, and other user characteristics affect users' preferences, enable defining gamification designs tailored to user and contextual features simultaneously, and provide technological aid for those interested in designing personalized gamification. The main implications are that demographics, game-related characteristics, geographic location, and LAT to be done, as well as the interaction between different kinds of information (user and contextual characteristics), should be considered in defining gamification designs and that personalizing gamification designs can be improved with aid from our recommender system.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • The Effect on Computational Thinking Using SRA-Programming: Anticipating
           Changes in a Dynamic Problem Environment

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      Authors: Nardie Fanchamps;Lou Slangen;Marcus Specht;Paul Hennissen;
      Pages: 213 - 222
      Abstract: This article illustrates that the task design and problem selection are of characteristic influence to evoke sense-reason-act programming (SRA) among primary school pupils when programming robots. Research shows that the task design influences the development of computational thinking (CT). The literature provides evidence that the context, the problem space, and the representation of the problem to apply SRA-programming require the programming task to be embedded in a dynamic context in which a programmable robot must use sensory information to anticipate changes in the environment. In order to ascertain whether the problem space and the task design influence the evocation of SRA-thinking, this article uses a research design comparing the differences between two programming conditions (static/dynamic). In these conditions, pupils use Lego EV-3 robots and Mindstorms software to solve programming problems. As a post-measurement, a Lego challenge is applied. In this article, it is shown that the integration of a dynamic task design to solve a programming problem is essential for a deeper understanding of CT skills. Furthermore, when pupils can immediately test the consequences of their program in a dynamic environment and, thus, the learning environment provides an appropriate problem, they gain a deeper understanding of the added value of sensors and will be better able to reason about complex problems. It is found that programming in a dynamic problem environment almost naturally evokes SRA-thinking, as opposed to programming in a static environment. The influence of SRA-programming as demonstrated identifies characteristics of CT.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Xiao-Shih: A Self-Enriched Question Answering Bot With Machine Learning on
           Chinese-Based MOOCs

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      Authors: Hao-Hsuan Hsu;Nen-Fu Huang;
      Pages: 223 - 237
      Abstract: This article introduces Xiao-Shih, the first intelligent question answering bot on Chinese-based massive open online courses (MOOCs). Question answering is critical for solving individual problems. However, instructors on MOOCs must respond to many questions, and learners must wait a long time for answers. To address this issue, Xiao-Shih integrates many novel natural language processing and machine learning approaches to achieve state-of-the-art performance. Furthermore, Xiao-Shih has a built-in self-enriched mechanism for expanding the knowledge base through open community-based question answering. This article proposes a novel approach, known as spreading question similarity (SQS), which iterates similar keywords on our keyword networks to find duplicate questions. Compared with BERT, an advanced neural language model, the results showed that SQS outperforms BERT on recall and accuracy above a prediction probability threshold of 0.8. After training, Xiao-Shih achieved a perfect correct rate. Furthermore, Xiao-Shih outperforms Jill Watson 1.0, which is a noted question answering bot, on answer rate with the self-enriched mechanism.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Addressing Student Fatigue in Computer Architecture Courses

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      Authors: Pablo Fuentes;Cristóbal Camarero;David Herreros;Vladimir Mateev;Fernando Vallejo;Carmen Martínez;
      Pages: 238 - 251
      Abstract: Understanding the architecture of a processor can be uninteresting and deterring for computer science students, since low-level details of computer architecture are often perceived to lack real-world impact. These courses typically have a strong practical component where students learn the fundamentals of the computer architecture and the handling of input/output operations through the development of simple programs in a low-level assembly programming language. Since these practical sessions require a strong involvement, student attendance and withdrawal rates are poor, lowering academic results and introducing a negative feedback loop that preconditions students to dislike them. This article introduces a new methodology for the practical sessions of Computer Organization and Design courses. This methodology disavows the use of simulators and focuses on actual hardware to promote a feeling of proximity to the execution and outcome of the programs. The proposed setup uses Raspberry Pi devices to encourage students to work autonomously, due to their low cost, capability of running an OS, and rich ecosystem of simple hardware devices. The setup is completed with RISC OS, which combines a simple window-based graphical interface with a low-level management of the hardware without requiring software abstraction layers. The article presents the methodology and the UCDebug tool, developed to help students debug their codes in RISC OS. After the introduction of the new setup at the University of Cantabria, academic results and student satisfaction have improved. The setup has also allowed to sustain a similar organization of the courses throughout the COVID-19 pandemic.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Hybrid Maximum Clique Algorithm Using Parallel Integer Programming for
           Uniform Test Assembly

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      Authors: Kazuma Fuchimoto;Takatoshi Ishii;Maomi Ueno;
      Pages: 252 - 264
      Abstract: Educational assessments often require uniform test forms, for which each test form has equivalent measurement accuracy but with a different set of items. For uniform test assembly, an important issue is the increase of the number of assembled uniform tests. Although many automatic uniform test assembly methods exist, the maximum clique algorithm (MCA)-based method is known to assemble the greatest number of uniform tests with the highest measurement accuracy based on the item response theory. In that method, the graph is constructed by sequentially adding a randomly formed test as a vertex without considering the graph structure. However, an important difficulty is its high space complexity, which interrupts search cliques with more than a hundred thousand vertices. To overcome this difficulty, this article proposes a new uniform test assembly algorithm: hybrid maximum clique algorithm using parallel integer programming. The first step searches a maximum clique that is as large as possible up to computer memory limitations using a state-of-the-art MCA with low time complexity but with high space complexity. The second step repeatedly searches a vertex connected with all vertices of the current maximum clique from the remaining vertices using integer programming with low space complexity but with high time complexity. The proposed method constructs a larger number of tests than the traditional methods do. Finally, we use simulation and actual data experiments to demonstrate the effectiveness of the proposed method. Results show that our method assembles a 1.5–2.7 times greater number of uniform tests than traditional methods can.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Assessing Learner Facilitation in MOOC Forums: A Mixed-Methods Evaluation
           Study

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      Authors: Anastasios Ntourmas;Yannis Dimitriadis;Sophia Daskalaki;Nikolaos Avouris;
      Pages: 265 - 278
      Abstract: One of the main challenges of massive open online courses (MOOCs) is the effective facilitation of learners in the course forum. The more learners participating in the forum, the more difficult it is for instructors to provide timely support. The effective intervention of teaching assistants (TAs) can play a crucial role in mitigating this issue; however, questions arise regarding the instructional approaches that TAs follow and whether they can effectively promote learning in a MOOC environment. In this article, we study the instructional approaches that TAs followed in two MOOCs of different subject matters, using mixed-methods. The goal was to evaluate the pedagogies that TAs adopted in facilitating learners, following a broadly accepted framework on MOOC instructional quality. Content analysis and interviews with the TAs provided insights into their intervention strategies, while linguistic and social network analysis enhanced the findings. The study revealed that the TAs most often were rushing to provide direct answers rather than guiding learners in finding themselves an answer. Problem-solving approaches and collaboration were not adequately promoted. The findings of this article provide design implications for the development of supportive tools that could automatically assess facilitators’ instructional approaches in the forum. Thus, timely feedback could be provided to instructors so as to refine the instructional design of their online courses and to provide guided support to their learner facilitators while courses are running.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Using Augmented Reality to Improve Learning Efficacy in a Mechanical
           Assembly Course

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      Authors: Peng-Fei Han;Feng-Kui Zhao;Gang Zhao;
      Pages: 279 - 289
      Abstract: Mechanical assembly courses are widely set up for mechanical and vehicle engineering majors. Teaching in these classes is traditionally presented in the form of 2-D lectures, which are ineffective for students to understand complex 3-D information. The study presented in this article aimed to investigate whether augmented reality (AR) could improve students’ learning efficacy in a mechanical assembly course. An AR-based assembly instruction app on mobile devices was developed, and a comparative research approach was adopted. A total of 104 junior students from four classes on automotive engineering at Nanjing Forestry University participated in the experiment during their professional skill training course. The control group was provided with handouts as usual, and the experimental group was required to use AR instructions during the assembly processes. Several evaluation indicators, such as assembly quality and speed, were recorded and analyzed at the same time. The results showed that such indicators were significantly improved with the utilization of AR. In addition, a questionnaire was specifically designed and distributed to all the students at the end of the course. It was found that several learning factors, such as learning interest and academic achievements in the experimental group, showed a positive correlation.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
  • Portable Joint Attention Skill Training Platform for Children With Autism

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      Authors: Vishav Jyoti;Uttama Lahiri;
      Pages: 290 - 300
      Abstract: Children with autism are characterized by milestones in joint attention (JA) skill. They fail to understand the directional cue issued by a partner (during social communication), which often results in them reciprocating inappropriately and not completing the JA bid successfully. The directional cues can be gaze-pointing, finger-pointing, etc., toward a target object of interest. Here, in this article, we present a TABlet-based Joint Attention Task (TABJAT) platform that can present JA tasks on a tablet with virtual characters (serving the role of JA administrator), delivering prompting cues (e.g., gaze-pointing, finger-pointing, and head orientation) and virtual objects offering sparkling cue. The platform is adaptive to one's JA skill (while autonomously offering JA tasks with gradually decreasing cueing prompt information, i.e., increasing the task difficulty). This can offer an opportunity for a child to interact with the JA tasks by himself/herself (preserving the triadic interaction regime of JA tasks) and learn the skill while being at home and/or school. Results of a study with 18 children with autism indicate the feasibility of TABJAT to be accepted by the target group, and quantify their JA skill in an individualized manner in terms of their task performance while adaptively offering the JA tasks of varying difficulty.
      PubDate: April 1 2022
      Issue No: Vol. 15, No. 2 (2022)
       
 
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