Forthcoming and Online First Articles

International Journal of Smart Technology and Learning

International Journal of Smart Technology and Learning (IJSMARTTL)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Smart Technology and Learning (6 papers in press)

Regular Issues

  • Bioinformatics Education for Undergraduates: The Need for Project-Based and Experiential Approaches   Order a copy of this article
    by Michael Wolyniak 
    Abstract: The -omics revolution and advances in DNA sequencing technology have made bioinformatics an essential tool for full participation in the rapidly-evolving life science research community. However, considerable barriers among undergraduate instructors and students have largely prevented the mainstream integration of bioinformatics into life science curricula. To overcome these barriers, several groups have developed collaborative initiatives with the goal of providing instructors with the skills, confidence, and resources that they need to successfully implement bioinformatics course modules to their students in an engaging manner. This paper considers some of these successful initiatives and offers ideas on how their work can be further expanded to make bioinformatics education a standard practice at the undergraduate level.
    Keywords: bioinformatics; -omics; high-throughput sequencing; undergraduate education; barriers; CUREs.

  • Leveraging AI, Big Data, and Educational Technology to Promote Collaborative Learning and Improve Cyberlearning Courses Synopsis and Linked Presentations of the Workshop at Orlando, Florida, June 4-6, 2019, and the Online Workshop August 13-14, 2020.   Order a copy of this article
    by Hong Liu, Xiaoqing Gu 
    Abstract: This article presents a synopsis and the summaries of a series of presentations delivered in two workshops that we organized in 2019 and 2020. The purpose of the two workshops is to find practical solutions to the emerged peer learning problems in a distributed learning (DL, or Cyberlearning) environment, which becomes popular under and after the COVID-19 pandemic. A DL environment not only consists of personal or virtual instructors, online courseware, communicational technologies, but also peer learners. Collaborative learning between peers in a distributed learning environment is inadequately addressed in research literature and paid insufficient attention in practices. In an active and constructive learning environment (Chi, & Wylie, 2014) such as team projects and research experiences for undergraduates (REU), learning from peers is as helpful as learning from the instructors. However, collaborative learning from remote peers is much more challenging than face-to-face teamwork. This synopsis can serve as a road map for readers to find your interested topics starting at the broad view of the peer-learning problem in a DL environment and practical solutions as first-hand experiences of 20 speakers. The summaries of the topics, the linked videos, PowerPoint slides, and the references will guide the readers to explore such a hardly treaded territory at a flexible pace, breadth, and depth.
    Keywords: Distributed Learning; Learning Space; Collaborative Learning' Learning Analytics; Educational Technology; Artificial Intelligence.

  • Task-Agnostic Team Competence Assessment and Metacognitive Feedback for Transparent Project-Based Learning in Data Science   Order a copy of this article
    by Hong Liu, Timothy Bernard, Elif Cankaya, Alex Hall 
    Abstract: Assessing team and individual competencies from team projects' outcomes alone can be pretty subjective. Sharing credit for team efforts equally between team members or differentiating individual contributions based on peer evaluations that might be prone to bias destroys motivation and hinders learning. A fair assessment of individual performances should depend on a formative assessment of a team's process and each individual's contribution to tasks. Such an assessment is time-consuming and only affordable to utilize in small classes. This research serves as a small step to synergize the human and Artificial Intelligence (AI) based educational technology to improve the transparency and effectiveness of collaborative Project-Based Learning (PBL). We introduce a web-bot (BotCaptain) to automate parts of the instructional tasks and present a task-agnostic team competency model and metadata to assess individual contributions in team PBL. With the use of the AI, a set of metacognitive feedback to each team members are also discussed. Study findings have implications for the use of AI in PBL environments.
    Keywords: Computer Supported Collaborative Learning; Teamwork Process; learning assessment; Metacognition; Experience API (xAPI); Artificial Intelligence.

  • Personalized Emotion-Aware e-Learning Systems with Interventions   Order a copy of this article
    by Zahra Karamimehr, Mohammad Mehdi Sepehri, Soheil Sibdari, Toktam Khatibi, Hassan Aghajani 
    Abstract: Personalized education, automated tutoring, and targeted evaluation are among the top technology-intense advances in education today. Automated intervention in online student learning is essential in the absence of human instructors. These interventions need to consider non-observable features of e-learners and offer instructions according to the systems perception of these features. In this paper, we study the intervention methods in learning process according to the emotional state of e-learner. We adopt Control-Value Theory of achievement emotions as our research basis to infer about the affective state of e-learners. We offer educational and affective strategies based on the learning behavior of the e-learner and determine the time to intervene in addition to the type of support and materials that are required in each intervention. The efficiency of our proposed personalized system is evaluated by conducting an experiment in a real e-learning platform using three learning metrics learning gains, course engagement, and satisfactions.
    Keywords: achievement emotions; emotion-aware personalization; human-computer interaction; intelligent tutoring systems; online learning behaviors; smart learning; the control-value theory.

  • Human and Artificial Intelligence in Education   Order a copy of this article
    by J. Michael Spector 
    Abstract: Many accounts describe and define human intelligence. Some common abilities include: learning solving problems, creating innovative solutions, remembering details, etc. Artificial intelligence (AI) has fewer descriptions and definitions, including these: performing things normally done by experienced humans, or assisting less experienced persons go perform well. We have learned a great deal in the last 50 years about both human intelligence and artificial intelligence. Researchers now believe there are billions of neurons in the brain and mapping how people use those neurons to react to situations and solve problems is an area of active exploration with many unknowns. Human intelligence has not progressed on a scale or order of magnitude to match the progress in AI. How things will both evolve? Will AI evolve to support humans including the development of human intelligence, or is there a darker path ahead? This article aims to cause focused deliberations going forward.
    Keywords: creativity; artificial intelligence; human intelligence; intelligence; problem solving.

  • A Machine Learning Based Crop Recommendation System and User-friendly Android Application for Cultivation   Order a copy of this article
    by Kaniz Fatima Tonni, Mahfuzulhoq Chowdhury 
    Abstract: Bangladesh is essentially an agricultural nation, and its economy is heavily dependent on it. A farmer could plant a crop if he knew which one would yield more. The existing literature works fails to provide a user-friendly mobile application for cultivation as well as machine learning based crop recommendation by taking different factors into account. This paper creates an mobile application that enables farmers to forecast viable crops based on climate factors like humidity, rainfall, and temperature as well as soil characteristics. The suggested model is used to forecast agricultural production using crop records of diverse crops with various properties of soil and climate parameters. The suggested model offers farmers a comprehensive list of recommendations to help them choose crops that are best for them based on particular considerations like production costs and fertilizer recommendations. The users feedback shows satisfactory remarks in terms of its usefulness.
    Keywords: Crop Recommendation; Machine Learning; Prediction; Evaluation; Bangladesh; Cultivation; Android Application.