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Jingili, N., Oyelere, S., Malmström Berghem, S., Brännström, R., Laine, T. H., Lindqvist, A.-K. & Rutberg, S. (2024). A Two-Stage co-Design Process of Battleship-AST Persuasive Game for Active School Transportation in Northern Sweden. International Journal of Human-Computer Interaction
Open this publication in new window or tab >>A Two-Stage co-Design Process of Battleship-AST Persuasive Game for Active School Transportation in Northern Sweden
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2024 (English)In: International Journal of Human-Computer Interaction, ISSN 1044-7318, E-ISSN 1532-7590Article in journal (Refereed) Epub ahead of print
Abstract [en]

This research delves into the dynamics of active school transport (AST) by utilizing a two-stage co-design process and leveraging persuasive technology within a game for promoting AST called Battleship-AST. The primary aim of this research is to thoroughly investigate the two-stage game co-design process employed in creating a Battleship-AST game. Moreover, our research aims to evaluate participants’ perceptions regarding the motivating and engaging potential of the persuasive technology and gamification features embedded within the final iteration of the game. This evaluation aims to understand how these features influence participants’ motivation to increase their usage of AST through gameplay. In pursuit of these objectives, the research builds upon the existing Battleship-AST prototype and actively engages school children in a collaborative two-stage co-design process. Their valuable insights and preferences were harnessed in refining the game, which was subsequently tested during a tech event in Skellefteå, Sweden. The findings shed light on various aspects of the game’s impact, from its reception to the gamification features integrated within. Notably, the research highlights the positive impact of the co-design process, with increased motivation and engagement observed among the participants. Their involvement in shaping the game’s design resulted in a more engaging and enjoyable experience. The persuasive technology features, encompassing competition, collaboration, auditory cues, a virtual reward system, and an emphasis on similarity, played a pivotal role in sustaining engagement and motivating players. Elements such as rewards, leaderboard progression, and badges proved highly effective in encouraging continued participation and fostering a positive feedback loop. However, the study also identifies areas for potential improvement, including the need to measure real-life progress and refine the game’s levelling system. The research indicates that refining feedback mechanisms and tailoring game content to individual preferences could create an even more engaging experience. Additionally, long-term playtesting is proposed to assess the game’s extended impact. The findings offer promising avenues for enhancing motivation and engagement in AST, which can contribute to the promotion of healthier and more sustainable transportation choices among school children.

Place, publisher, year, edition, pages
Taylor & Francis, 2024
Keywords
Persuasive technology, gamification, games, active school transport, physical activity, co-design
National Category
Human Computer Interaction Media and Communication Technology
Research subject
Pervasive Mobile Computing; Physiotherapy
Identifiers
urn:nbn:se:ltu:diva-105698 (URN)10.1080/10447318.2024.2355395 (DOI)001232107000001 ()2-s2.0-85194546518 (Scopus ID)
Funder
Vinnova, 2020-01867
Note

Funder: Ministry of Education of the Republic of Korea; National Research Foundation of Korea [NRF-2023S1A5C2A02095195];

Fulltext license: CC BY

Available from: 2024-05-31 Created: 2024-05-31 Last updated: 2024-11-20
Mazana, M. Y., Montero, C. S., Olifage, R. C. & Oyelere, S. S. (2024). Designing and Testing a Contextual Factors-Based Teaching and Learning Model for Blended Mathematics Instruction. Contemporary Mathematics (Singapore), 5(3), 3900-3928
Open this publication in new window or tab >>Designing and Testing a Contextual Factors-Based Teaching and Learning Model for Blended Mathematics Instruction
2024 (English)In: Contemporary Mathematics (Singapore), ISSN 2705-1064, Vol. 5, no 3, p. 3900-3928Article in journal (Refereed) Published
Abstract [en]

The study employs design-based research (DBR) to design the mathematics teaching and learning activity model (M-TLAM) for blended learning instruction in a Tanzanian higher education context. This model utilises contextual factors, including ICT tool usage, collaborative learning with metacognitive activities, and local culture, to determine how optimising these factors boosts math students' motivation and achievement. Two DBR phases were conducted, of which Phase 1 lasted for two weeks and Phase 2 an entire semester. To evaluate M-TLAM in real-life learning settings, experiments were conducted with 225 first-year undergraduate students and seven lecturers at the  College of Business Education in Tanzania. Experimental data were collected from pre-and post-tests, interviews, and questionnaires administered to students and lecturers. The study examined the perceptions and motivation of the participating lecturers towards using the M-TLAM in mathematics education and the factors influencing student motivation, academic achievement, and experience towards business mathematics courses. The evaluation results are promising and show that the M-TLAM implementation can potentially improve students' motivation and academic achievement. In addition, the pedagogical experiences of students were primarily positive, and student's attitudes towards the business mathematics course through M-TLAM were more favourable than those of students who studied the course via traditional methods. Cultural, technological, and instructional Factors contribute to students' improved motivation. Lectures demonstrated a cheerful disposition towards the potential of the M-TLAM for enhancing teaching and learning activities. The contributions of this study are highlighted through the implementation of the M-TLAM for blended learning and through the design principles and guidelines provided towards its effective implementation.

Place, publisher, year, edition, pages
Universal Wiser Publisher, 2024
Keywords
blended learning model, flipped classroom, higher education, M-TLAM, mathematics, motivation, learning achievement
National Category
Didactics Learning
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-110274 (URN)10.37256/cm.5320243176 (DOI)001325266600013 ()2-s2.0-85205234446 (Scopus ID)
Note

Validerad;2024;Nivå 1;2024-10-07 (sarsun);

Full text license: CC BY 4.0;

Available from: 2024-10-07 Created: 2024-10-07 Last updated: 2024-11-20Bibliographically approved
Olugbade, D., Oyelere, S. S. & Agbo, F. J. (2024). Enhancing junior secondary students' learning outcomes in basic science and technology through PhET: A study in Nigeria. Education and Information Technologies: Official Journal of the IFIP technical committee on Education, 29, 14035-14057
Open this publication in new window or tab >>Enhancing junior secondary students' learning outcomes in basic science and technology through PhET: A study in Nigeria
2024 (English)In: Education and Information Technologies: Official Journal of the IFIP technical committee on Education, ISSN 1360-2357, E-ISSN 1573-7608, Vol. 29, p. 14035-14057Article in journal (Refereed) Published
Abstract [en]

A computer-based simulation is a viable approach for integrating the basics of science and technology in Junior Secondary. This study examined the impact of PhET on students' academic performance as well as students' motivation toward Basic Science and Technology. The study also investigates how PhET influence students' attitudes toward Basic Science and Technology. The study adopted the pre-test, post-test, and non-equivalent control group design. We randomly selected the study population from junior secondary two (JSII) students in private and public schools who are studying Basic Science and Technology. Quantitative data were collected and analyzed using a statistical approach such as the mean, standard deviation, and t-test. Students' post-test academic performance improved significantly (t160 = 36.28, p < 0.05) as a result of teaching Basic Science and Technology with PhET. According to the findings PhET had a substantial effect on the motivation of Basic Science and Technology students (t160 = 29.32, p < 0.05). Furthermore, the results demonstrated that PhET affected students' attitudes toward Basic Science and Technology (t160 = 65.36, p < 0.05). This study contributes to the body of knowledge by providing empirical evidence to support the integration of PhET in the teaching of Basic Science and Technology in Nigeria and other similar contexts. The findings suggest that PhET can be an effective pedagogical tool for improving learning outcomes in science and technology education, particularly in developing countries where resources and infrastructure may be limited.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Computer-Based Interactive Simulation (CBIS), Simulation-based learning, STEM Education, Simulation-based education (SBE), Basic Science and Technology, Nigeria
National Category
Learning Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-103474 (URN)10.1007/s10639-023-12391-3 (DOI)001132718300002 ()2-s2.0-85181220765 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-09-11 (joosat);

Available from: 2024-01-04 Created: 2024-01-04 Last updated: 2024-12-16Bibliographically approved
Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., . . . De Albuquerque, V. H. (2024). Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey. IEEE Access, 12, 35796-35812
Open this publication in new window or tab >>Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey
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2024 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 35796-35812Article, review/survey (Refereed) Published
Abstract [en]

The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2024
Keywords
big data, Drug discovery, explainable artificial intelligence, machine learning
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-104882 (URN)10.1109/ACCESS.2024.3373195 (DOI)001184733800001 ()2-s2.0-85187337752 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-05 (marisr);

Full text license: CC BY

Available from: 2024-03-26 Created: 2024-03-26 Last updated: 2024-11-20Bibliographically approved
Yousefdeh, S. A. & Oyelere, S. S. (2024). Investigating co-presence and collaboration dynamics in realtime virtual reality user interactions. Frontiers in Virtual Reality, 5, Article ID 1478481.
Open this publication in new window or tab >>Investigating co-presence and collaboration dynamics in realtime virtual reality user interactions
2024 (English)In: Frontiers in Virtual Reality, E-ISSN 2673-4192, Vol. 5, article id 1478481Article in journal (Refereed) Published
Abstract [en]

As Virtual Reality (VR) technologies advance and gain popularity, their potential as powerful tools for collaboration is increasingly recognized. VR facilitates interaction with the virtual presence of individuals who are not physically co-located. Understanding the dynamics of user interactions and the cognitive perception of virtual presence quality is essential for this technology’s progression. This paper introduces CoCoVR, a VR measurement method for measuring the sense of co-presence and collaboration quality among users through real-time data collection and analysis. CoCoVR is evaluated across various scenarios to understand user interactions in VR under different conditions. An extensive analysis of recent literature has been performed that identified avatar realism and communication as two key factors influencing co-presence and collaboration. The experiment includes a custom VR application, the Soma cube puzzle, and real-time sensors. A between-subject experiment was conducted to collect and analyzes real-time data on collaboration and co-presence. This study integrates both objective and subjective measures, offering deeper insights into the immersive experience and its impact on collaborative tasks. The findings show that avatar realism enhances the feeling of co-presence and that communication methods substantially improve collaboration. Additionally, the study found that measuring physiological responses can serve as a novel method for evaluating the quality of user collaborations.

Place, publisher, year, edition, pages
Frontiers Media SA, 2024
Keywords
VR, virtual reality, collaboration, co-presence, sense of presence, data analysis, collaborative virtual environment, virtual environment
National Category
Human Computer Interaction
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-111176 (URN)10.3389/frvir.2024.1478481 (DOI)001378899200001 ()2-s2.0-85212302385 (Scopus ID)
Note

Validerad;2025;Nivå 1;2025-02-05 (u5);

Full text license: CC BY 4.0;

Available from: 2025-02-05 Created: 2025-02-05 Last updated: 2025-02-05Bibliographically approved
Sanusi, I. T., Sunday, K., Oyelere, S. S., Suhonen, J., Vartiainen, H. & Tukiainen, M. (2024). Learning machine learning with young children: exploring informal settings in an African context. Computer Science Education, 34(2), 161-192
Open this publication in new window or tab >>Learning machine learning with young children: exploring informal settings in an African context
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2024 (English)In: Computer Science Education, ISSN 0899-3408, E-ISSN 1744-5175, Vol. 34, no 2, p. 161-192Article in journal (Refereed) Published
Abstract [en]

Background and context

Researchers have been investigating ways to demystify machine learning for students from kindergarten to twelfth grade (K–12) levels. As little evidence can be found in the literature, there is a need for additional research to understand and facilitate the learning experience of children while also considering the African context.

Objective

The purpose of this study was to explore how young children teach and develop their understanding of machine learning based technologies in playful and informal settings.

Method

Using a qualitative methodological approach through fine-grained analysis of video recordings and interviews, we analysed how 18 children aged 3–13 years constructed their interactions with a machine-based technology (Google’s Teachable Machine).

Findings

This study provides empirical support for the claim that Google’s Teachable Machine contributes to the development of data literacy and conceptual understanding across K–12 irrespective of the learners’ backgrounds. The results also confirmed children’s ability to infer the relationship between their own expressions and the output of the machine learning-based tool, thus, identifying the input-output relationships in machine learning. In addition, this study opens a discussion around differentials in emerging technology use across different contexts through participatory learning.

Implications

The results provide a baseline for future research on the topic and preliminary evidence to discern how children learn about machine learning in the African K–12 context.

Place, publisher, year, edition, pages
Taylor & Francis, 2024
Keywords
Machine learning, data, young children, participatory learning, informal settings, Africa
National Category
Learning
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-95571 (URN)10.1080/08993408.2023.2175559 (DOI)000928683100001 ()2-s2.0-85147753522 (Scopus ID)
Note

Validerad;2024;Nivå 1;2024-05-22 (joosat);

Full text license: CC BY

Available from: 2023-02-09 Created: 2023-02-09 Last updated: 2024-05-22Bibliographically approved
Qushem, U. B., Oyelere, S., Akçapınar, G., Kaliisa, R. & Laakso, M.-J. (2024). Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science. Technology, Knowledge and Learning, 29, 1385-1400
Open this publication in new window or tab >>Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science
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2024 (English)In: Technology, Knowledge and Learning, ISSN 2211-1662, E-ISSN 2211-1670, Vol. 29, p. 1385-1400Article in journal (Refereed) Published
Abstract [en]

Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students’ early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine learning algorithms with predictive analytics has proven possible and meaningful. The traditional approach of looking after students without uncovering the root causes of poor performance has shifted dramatically into improving the quality of the educational processes of students, teachers, and stakeholders. Thus, this study employed predictive analytics to develop an early warning prediction model using computing science degree performance data at a public institution. Predictive models based on our data analysis revealed that low, medium, and high-performing students could be predicted with an accuracy of 88% using only the grades of the courses they took in the second year. Moreover, 96% accuracy was achieved when all course grades were used in predictive models. The courses that are important in determining the overall performance of the students were also analyzed. By employing a multi-method approach, utilizing a large dataset spanning four academic years, and including a diverse sample of 430 students, our study offers a robust foundation to researchers, designers, and computer science educators for understanding and predicting student performance. The enhanced generalizability and implications for educational practice position our study as a valuable contribution to the field, paving the way for further advancements in predictive analytics.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Early warning systems, Predictive analytics, At-risk students, Machine learning, Classification
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-99219 (URN)10.1007/s10758-023-09674-6 (DOI)001031400100001 ()2-s2.0-85164916680 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-10-11 (joosat);

Available from: 2023-07-18 Created: 2023-07-18 Last updated: 2024-10-11Bibliographically approved
Sanusi, I. T., Oyelere, S., Vartiainen, H., Suhonen, J. & Tukiainen, M. (2023). A systematic review of teaching and learning machine learning in K-12 education. Education and Information Technologies: Official Journal of the IFIP technical committee on Education, 28(5), 5967-5997
Open this publication in new window or tab >>A systematic review of teaching and learning machine learning in K-12 education
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2023 (English)In: Education and Information Technologies: Official Journal of the IFIP technical committee on Education, ISSN 1360-2357, E-ISSN 1573-7608, Vol. 28, no 5, p. 5967-5997Article in journal (Refereed) Published
Abstract [en]

The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field. 

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Machine learning, Artificial intelligence, K-12, Systematic review
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-93828 (URN)10.1007/s10639-022-11416-7 (DOI)000879321200001 ()2-s2.0-85141344461 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-06-28 (sofila);

Available from: 2022-11-07 Created: 2022-11-07 Last updated: 2023-06-28Bibliographically approved
Jingili, N., Oyelere, S., Nyström, M. B. T. & Anyshchenko, L. (2023). A systematic review on the efficacy of virtual reality and gamification interventions for managing anxiety and depression. Frontiers in Digital Health, 5, Article ID 1239435.
Open this publication in new window or tab >>A systematic review on the efficacy of virtual reality and gamification interventions for managing anxiety and depression
2023 (English)In: Frontiers in Digital Health, E-ISSN 2673-253X, Vol. 5, article id 1239435Article in journal (Refereed) Published
Abstract [en]

This systematic review aims to assess the effectiveness of virtual reality (VR) and gamification interventions in addressing anxiety and depression. The review also seeks to identify gaps in the current VR treatment landscape and provide guidelines for future research and development. A systematic literature search was conducted using Scopus, Web of Science, and PubMed databases, focusing on studies that utilized VR and gamification technology to address anxiety and depression disorders. A total of 2,664 studies were initially identified, 15 of those studies fulfilled the inclusion criteria for this systematic review. The efficacy of VR in addressing anxiety and depression was evident across all included studies. However, the diversity among VR interventions highlights the need for further investigation. It is advised to incorporate more diverse participant samples and larger cohorts and explore a broader spectrum of therapeutic approaches within VR interventions for addressing anxiety and depression to enhance the credibility of future research. Additionally, conducting studies in varying socioeconomic contexts would contribute to a more comprehensive understanding of their real-world applicability.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
anxiety, depression, virtual reality, randomized controlled trials, mental health
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing; Psychology
Identifiers
urn:nbn:se:ltu:diva-102337 (URN)10.3389/fdgth.2023.1239435 (DOI)001104959600001 ()38026832 (PubMedID)2-s2.0-85177470078 (Scopus ID)
Funder
Luleå University of Technology, Luleå LTU-3515-2022
Note

Validerad;2023;Nivå 2;2023-11-08 (joosat);

CC BY 4.0 License

Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2024-11-20Bibliographically approved
Jingili, N., Oyelere, S. S., Malmström Berghem, S., Brännström, R., Laine, T. H. & Balogun, O. S. (2023). Adolescents’ perceptions of active school transport in northern Sweden. Heliyon, 9(10), Article ID e20779.
Open this publication in new window or tab >>Adolescents’ perceptions of active school transport in northern Sweden
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2023 (English)In: Heliyon, E-ISSN 2405-8440, Vol. 9, no 10, article id e20779Article in journal (Refereed) Published
Abstract [en]

Active school transport (AST) refers to using active means of transport such as walking, cycling, or riding a non-motorised scooter to school. It can help improve adolescents’ physical activity levels and create a more sustainable environment. The study involved 70 adolescents (45 boys and 25 girls) aged 13 to 14 from one school in Skellefteå, in Northern Sweden. In an online questionnaire, they were asked about their perceptions of cycling, walking, and riding a non-motorised scooter to school. This study used descriptive statistics, multiple regression analysis, and hypothesis testing with ANOVA to analyse the collected data and compare the perceptions of different types of transport on safety, environmental, and personal factors among adolescents in Northern Sweden. According to the results, more adolescents walked to school than cycled, and significantly few rode a non-motorised scooter to school. Most adolescents believe walking or cycling to school is a great way to exercise. Furthermore, the study also revealed that many adolescents avoid using AST due to the time it takes. Although the study showed that adolescents felt sufficient support for using AST from schools and parents, the number of adolescents using motorised transport is higher during winter than in summer. Additionally, most of them were more confident about cycling and walking to school than riding a non-motorised scooter and thought using AST was nice. Finally, most adolescents perceived having complete control over their transport options when going to school. The research indicates that it is crucial to implement interventions that inspire children to be interested and excited about using AST. These strategies should include fostering an AST culture that is fun and positive, as well as creating environments that are safe and supportive. The research results will guide the creation of a persuasive game that can motivate adolescents to use AST and measure its effectiveness.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Active school transport, Adolescent, Cycling, Physical activity, Riding a non-motorised scooter, Walking
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-101973 (URN)10.1016/j.heliyon.2023.e20779 (DOI)001110482800001 ()37860541 (PubMedID)2-s2.0-85173220265 (Scopus ID)
Funder
Vinnova, 2020-01867
Note

Validerad;2023;Nivå 2;2023-11-07 (sofila);

Funder: International Cooperation Joint Research Fund of Ajou University (S-2023-G0001-00020);

License full text: CC BY

Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2024-11-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9895-6796

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