Exploring Pre-Service Teachers’ Perspectives on the Integration of Digital Game-Based Learning for Sustainable STEM Education
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1. Introduction
Understanding the determinants influencing pre-service teachers’ viewpoints is crucial for informed curriculum development, teacher training programs, and policy initiatives. The current literature lacks a comprehensive exploration of the multifaceted elements that contribute to pre-service teachers’ attitudes and beliefs regarding the integration of DGBL. This study seeks to address this gap by investigating the diverse factors, encompassing technological proficiency and potential to learn with digital games, that collectively influence how pre-service teachers perceive and approach the use of DGBL in STEM higher education.
2. Literature Review
2.1. STEM Education and Digital Literacy in South Africa
2.2. Digital Game-Based Learning in Higher Education
2.3. Technology Acceptance Model
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External Variables (Moderators): External factors that can influence the relationship between the main constructs.
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Perceived Usefulness (PU): The degree to which a person believes that using a particular technology would enhance their job performance or make it easier.
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Perceived Ease of Use (PEoU): The extent to which a person believes that using a particular system would be free of effort.
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Attitude Toward Using (AT): The individual’s overall positive or negative feelings about using the technology, which is influenced by perceived usefulness and ease of use.
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Behavioural Intention to Use (BI): The individual’s subjective probability that they will perform a particular behaviour, in this case, using the technology.
2.4. Theoretical Framework and Hypotheses
3. Methodology
3.1. Research Design
The study adopted a quantitative survey research design. A survey questionnaire was used in the data collection process. Descriptive statistics and partial least squares-structural equation modeling were used to explore the data and in the assessment of the above formulated hypotheses.
3.2. Participants
3.3. Instrument
A two-section questionnaire was employed to collect data from the respondents on their perception and intention to use DGBL. The first section gathered demographics of respondents and the second section consisted of a five-point Likert scale to collect data on the construct indicators. The indicator items were adopted and adapted from different literature sources. The Likert scale was used to measure the extent of the respondents’ agreement on the indicator statements that ranged from 1 as Strongly disagree to 5 as Strongly agree. Data were collected by means of random sampling and a survey questionnaire administered amongst STEM pre-service teachers at a university in South Africa.
3.4. Data Analysis Technique
4. Results
4.1. Descriptive Statistics
4.2. Measurement Model
4.3. Structural Model
The model accounts for 59% of the variance in DGBL behavioural intention. The exogenous variable explained 46% of the variance on perceived usefulness, and 21% on perceived ease of use, suggesting that the external variables played a positive and significant influence on the model’s perceived ease of use and perceived usefulness. Perceived ease of use, perceived usefulness, and attitude directly and positively influenced pre-service teachers’ behavioural intention to use DGBL. The results indicate that STEM pre-service educators agree that it is within their intention to use DGBL for learning and teaching. The model’s total effect demonstrated that behavioural intention was positively influenced by all proposed factors.
5. Discussion and Implications
The findings contribute to the potential impact of learning strategies, learning opportunity, and cognitive engagement towards DGBL adoption in South Africa universities.
6. Conclusions
This study has delved into the critical realm of digital game-based learning (DGBL) within the context of higher education, focusing specifically on the perceptions of pre-service teachers in a South African university. The increasing demand for self-directed learning, coupled with the imperative to sustain pedagogies that cater to diverse social contexts, underscores the significance of integrating STEM education content into digital games. This integration not only fosters digital societies but also provides contextualised learning opportunities and access to digital resources, particularly in developing countries such as those in Africa. The research has employed the Technology Acceptance Model (TAM) as a theoretical framework, investigating the acceptance and perceptions of pre-service teachers towards DGBL. The utilization of a quantitative survey design involving 255 participants revealed a favorable acceptance of DGBL among pre-service teachers. The model, validated through Partial Least Squares Structural Equation Modeling analysis, demonstrated a commendable explanatory strength of 59%, affirming the robustness of the proposed framework.
Significant insights emerged from the findings, highlighting positive perceptions of pre-service teachers towards DGBL. Notably, perceived learning opportunities, learning engagement, and learning strategies played pivotal roles in shaping these perceptions. The study unveiled a strong inclination among pre-service educators for DGBL as an approach that offers diverse learning opportunities, high engagement levels, and contextualised learning experiences. These findings contribute substantially to the broader fields of research in educational game design and implementation. The implications for contextualised self-directed learning with DGBL are profound, especially in regions where digital education is still in the nascent stages of acceptance. As the digital landscape continues to evolve, the insights gained from this study provide valuable guidance for future research endeavors in curriculum development, and the design of educational interventions that leverage digital games for sustainable development of diverse digital skills and lifelong learning.
The survey scope of study was, however, limited to one university and does not advance to the actual use of DGBL, but rather uses statistical models to predict its use through behavioural intention. This could result in limited generalization of the findings. The study is geographically limited to South Africa, and the northern KwaZulu Natal province, a rural region. In essence, this research not only reinforces the viability of DGBL as a pedagogical tool but also underscores its potential to address the unique challenges faced by developing countries.
As we move forward, it is imperative to build upon these findings, fostering a collaborative effort between researchers, educators, and policymakers to further integrate DGBL into educational practices. Therefore, future studies can replicate the investigation towards the actual use of DGBL across faculties and/or universities, thereby addressing and nurturing a generation equipped with the digital skills necessary for the complexities of the modern world.
Author Contributions
Conceptualization, N.M.G., D.S. and A.C.; methodology, N.M.G.; software, N.M.G. and A.C.; validation, N.M.G., D.S. and A.C.; formal analysis, N.M.G. and A.C.; investigation, N.M.G. and A.C.; resources, N.M.G.; data curation, N.M.G.; writing—original draft preparation, N.M.G.; writing—review and editing, N.M.G., D.S. and A.C.; visualization, N.M.G. and A.C.; supervision, D.S. and A.C.; project administration, D.S. and A.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of and approved by the Institutional Review Board (or Ethics Committee) of University of Zululand Research Ethics Committee (UZREC 171110-030 PGD 2019/26 and approved: 07-11-2022).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Conflicts of Interest
The authors declare no conflict of interest.
References
- Herselman, M.; Botha, A. ICT4REDResearch Framework; CSIR Meraka: Pretoria, South Africa, 2013. [Google Scholar]
- Lyons, A.C.; Zucchetti, A.; Kass-Hanna, J.; Cobo, C. Bridging the Gap between Digital Skills and Employability for Vulnerable Populations; G20-Japan: Urbana, IL, USA, 2019. [Google Scholar]
- Ishak, S.A.; Din, R.; Hasran, U.A. Defining Digital Game-Based Learning for Science, Technology, Engineering, and Mathematics: A New Perspective on Design and Developmental Research. J. Med. Internet Res. 2021, 23, e20537. [Google Scholar] [CrossRef] [PubMed]
- Nikou, S.A.; Economides, A.A. Continuance Intention to Use Mobile Learning in Terms of Motivation and Technology Acceptance. In Research on E-Learning ICT in Education: Technological, Pedagogical Instructional Perspectives; Tsiatsos, T., Demetriadis, S., Mikropoulos, A., Dagdilelis, V., Eds.; Springer: Berlin/Heidelberg, Germany, 2021; pp. 1–14. [Google Scholar]
- Hébert, C.; Jenson, J.; Terzopoulos, T. “Access to technology is the major challenge”: Teacher perspectives on barriers to DGBL in K-12 classrooms. E-Learn. Digit. Media 2021, 18, 307–324. [Google Scholar] [CrossRef]
- Kong, S.C.; Chan, T.-W.; Griffin, P.; Hoppe, U.; Huang, R.; Kinshuk; Looi, C.K.; Milrad, M.; Norris, C.; Nussbaum, M. E-learning in school education in the coming 10 years for developing 21st century skills: Critical research issues and policy implications. J. Educ. Technol. 2014, 17, 70–78. [Google Scholar]
- Acquah, E.O.; Katz, H.T. Digital game-based L2 learning outcomes for primary through high-school students: A systematic literature review. Comput. Educ. Libr. 2020, 143, 103667. [Google Scholar] [CrossRef]
- Chen, C.-H.; Shih, C.-C.; Law, V. The effects of competition in digital game-based learning (DGBL): A meta-analysis. Educ. Technol. Res. Dev. 2020, 68, 1855–1873. [Google Scholar] [CrossRef]
- Hightower, A.; Smith, N.; Wiens, P. How we prepare teachers for game-based learning: A mixed-methods teacher education study with Minecraft: Education Edition. In Proceedings of the Society for Information Technology & Teacher Education International Conference, Online, 29 March 2021; pp. 415–419. [Google Scholar]
- UNICEF-Ghana. Risks and Opportunities Related to Children’s Online Practices: Ghana Country Report; GlobalKidsOnline: London, UK, 2017; Available online: http://globalkidsonline.net/wp (accessed on 4 October 2022).
- Abrahams, L.; Burke, M. Report on the International Experience in Open Digital Governance; Prepared for the SA–EU Strategic Partnership Dialogue Facility. 2022. Available online: https://www.wits.ac.za/media/wits-university/research/tayarisha/documents/SA-EU-Dialogue-ODG_International-Experience.pdf (accessed on 30 November 2023).
- Greenop, K.; Busa, D. Developing educational games for mobile phones in South Africa. In Proceedings of the EdMedia + Innovate Learning, Vienna, Austria, 30 June 2008; pp. 6171–6181. [Google Scholar]
- Ndlovu, T.N.; Mhlongo, S. An investigation into the effects of gamification on students’ situational interest in a learning environment. In Proceedings of the 2020 IEEE Global Engineering Education Conference (EDUCON), Porto, Portugal, 27–30 April 2020; pp. 1187–1192. [Google Scholar]
- Chibisa, A.; Mutambara, D. An Exploration of Stem Students’ and Educators’ Behavioural Intention to Use Mobile Learning. J. E-Learn. Knowl. Soc. 2022, 18, 166–176. [Google Scholar]
- Xala, N. The Current State of Free Public WiFi in South Africa. 2018. Available online: https://htxt.co.za/2018/09/11/the-current-state-of-free-public-wifi-in-south-africa/ (accessed on 2 April 2022).
- Bayeck, R.Y. Exploring video games and learning in South Africa: An integrative review. Educ. Technol. Res. Dev. 2020, 68, 2775–2795. [Google Scholar] [CrossRef]
- Adukaite, A.; Van Zyl, I.; Er, Ş.; Cantoni, L. Teacher perceptions on the use of digital gamified learning in tourism education: The case of South African secondary schools. Comput. Educ. 2017, 111, 172–190. [Google Scholar] [CrossRef]
- Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
- Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef]
- Egenfeldt-Nielsen, S. Overview of research on the educational use of video games. Nord. J. Digit. Lit. 2006, 1, 184–214. [Google Scholar] [CrossRef]
- Zaibon, S.B.; Shiratuddin, N. Adapting learning theories in mobile game-based learning development. In Proceedings of the 2010 Third IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, Kaohsiung, Taiwan, 12–16 April 2010; pp. 124–128. [Google Scholar]
- Bourgonjon, J.; Valcke, M.; Soetaert, R.; Schellens, T. Students’ perceptions about the use of video games in the classroom. Comput. Educ. Libr. 2010, 54, 1145–1156. [Google Scholar] [CrossRef]
- Ibrahim, R.; Yusoff, R.C.M.; Khalil, K.; Jaafar, A. Factors affecting undergraduates’ acceptance of educational game: An application of technology acceptance model (TAM). In Proceedings of the International Visual Informatics Conference, Selangor, Malaysia, 9–11 November 2011; pp. 135–146. [Google Scholar]
- Bourgonjon, J.; De Grove, F.; De Smet, C.; Van Looy, J.; Soetaert, R.; Valcke, M. Acceptance of game-based learning by secondary school teachers. Comput. Educ. 2013, 67, 21–35. [Google Scholar] [CrossRef]
- Hinojo-Lucena, F.-J.; Dúo-Terrón, P.; Ramos Navas-Parejo, M.; Rodríguez-Jiménez, C.; Moreno-Guerrero, A.-J. Scientific Performance and Mapping of the Term STEM in Education on the Web of Science. Sustainability 2020, 12, 2279. [Google Scholar] [CrossRef]
- Oblinger, D.; Oblinger, J.L.; Lippincott, J.K. Educating the Net Generation; EDUCAUSE: Boulder, CO, USA, 2005. [Google Scholar]
- Chapman, P.; Selvarajah, S.; Webster, J. Engagement in multimedia training systems. In Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, HICSS-32, Abstracts and CD-ROM of Full Papers, Maui, HI, USA, 5–8 January 1999; p. 9. [Google Scholar]
- Khan, A.; Ahmad, F.H.; Malik, M.M. Use of digital game based learning and gamification in secondary school science: The effect on student engagement, learning and gender difference. Educ. Inf. Technol. 2017, 22, 2767–2804. [Google Scholar] [CrossRef]
- Titus, S.; Ng’ambi, D. Exploring the use of digital gaming to improve student engagement at a resource poor institution in South Africa. In Proceedings of the European Conference on Games Based Learning, Berlin, Germany, 9–10 October 2014; Volume 2, pp. 742–748. [Google Scholar]
- Agarwal, R.; Karahanna, E. Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Q. 2000, 24, 665–694. [Google Scholar] [CrossRef]
- Ibrahim, R.; Masrom, S.; Yusoff, R.; Zainuddin, N.; Rizman, Z. Student acceptance of educational games in higher education. J. Fundam. Appl. Sci. 2017, 9, 809–829. [Google Scholar] [CrossRef]
- Fredricks, J.A.; Blumenfeld, P.C.; Paris, A.H. School engagement: Potential of the concept, state of the evidence. Rev. Educ. Res. 2004, 74, 59–109. [Google Scholar] [CrossRef]
- Saadé, R.; Bahli, B. The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Inf. Manag. Sci. 2005, 42, 317–327. [Google Scholar] [CrossRef]
- Lemay, D.; Doleck, T.; Bazelais, P. The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students. Eurasia J. Math. Sci. Technol. Educ. 2018, 14, em1635. [Google Scholar]
- Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford Publications: New York, NY, USA, 2015. [Google Scholar]
- George, D.; Mallery, P. IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference; Routledge: New York, NY, USA, 2019. [Google Scholar]
- Hair, J.F.; Hult, T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Thousand Oaks, CA, USA, 2016. [Google Scholar]
- Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
- Hair, J.F.; Ringle, C.M.; Sarstedt, M. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Plan. 2013, 46, 1–12. [Google Scholar] [CrossRef]
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Vanduhe, V.Z.; Nat, M.; Hasan, H.F. Continuance Intentions to Use Gamification for Training in Higher Education: Integrating the Technology Acceptance Model (TAM), Social Motivation, and Task Technology Fit (TTF). IEEE Access 2020, 8, 21473–21484. [Google Scholar] [CrossRef]
- Dele-Ajayi, O.; Strachan, R.; Anderson, E.V.; Victor, A.M. Technology-Enhanced Teaching: A Technology Acceptance Model to Study Teachers’ Intentions to Use Digital Games in the Classroom. In Proceedings of the 2019 IEEE Frontiers in Education Conference (FIE), Covington, KY, USA, 16–19 October 2019; pp. 1–8. [Google Scholar]
- Acosta-Gonzaga, E.; Walet, N.R. The role of attitudinal factors in mathematical on-line assessments: A study of undergraduate STEM students. Assess. Eval. High. Educ. 2018, 43, 710–726. [Google Scholar] [CrossRef]
- Chiu, T.K. Introducing electronic textbooks as daily-use technology in schools: A top-down adoption process. Br. J. Educ. Technol. 2017, 48, 524–537. [Google Scholar] [CrossRef]
- Blume, C. Games people (don’t) play: An analysis of pre-service EFL teachers’ behaviors and beliefs regarding digital game-based language learning. Comput. Assist. Lang. Learn. 2020, 33, 109–132. [Google Scholar] [CrossRef]
Technology acceptance model [18].
Figure 2.
The proposed DGBL technology acceptance model.
Figure 2.
The proposed DGBL technology acceptance model.
Figure 3.
Structural model.
Figure 3.
Structural model.
Table 1.
Hypotheses overview: statements and proposed relationships.
Table 1.
Hypotheses overview: statements and proposed relationships.
Hypothesis | Path | Hypothesis Statement |
---|---|---|
H-1: | BA > BI | Behavioural attitude has a significant influence on pre-service teacher intention to use DGBL. |
H-2: | PU > BA | Perceived usefulness has a significant influence on pre-service teacher behavioural attitude. |
H-3: | PEoU > BA | Perceived ease of use has a significant influence on pre-service teacher behavioural attitude. |
H-4: | PEoU > PU | Perceived ease of use has a significant influence on pre-service teacher perceived usefulness. |
H-5: | LS > PU | Learning strategies have significant influence on pre-service teacher perceived usefulness perceived. |
H-6: | LS > PEoU | Learning strategies have significant influence on pre-service teacher perceived ease of use. |
H-7: | LO > PU | Learning opportunities have significant influence on pre-service teacher perceived usefulness. |
H-8: | LO > PEoU | Learning opportunities have significant influence on pre-service teacher perceived ease of use. |
H-9: | CE > PU | Cognitive engagement has significant influence on pre-service teacher perceived usefulness. |
H-10: | CE > PEoU | Cognitive engagement has significant influence on pre-service teacher perceived ease of use. |
Table 2.
Average score values for each scale and Fornell–Larcker criterion values (in bold); greater values mean greater correlation between constructs.
Table 2.
Average score values for each scale and Fornell–Larcker criterion values (in bold); greater values mean greater correlation between constructs.
Construct | Mean | SD | BA | CE | BI | LO | LT | PEoU | PU |
---|---|---|---|---|---|---|---|---|---|
Behavioural attitude (BA) | 0.830 | ||||||||
Cognitive Engagement (CE) | 3.68 | 0.99 | 0.467 | 0.801 | |||||
Intention to use DGBL | 3.67 | 1.08 | 0.746 | 0.442 | 0.802 | ||||
Learning Opportunities (LO) | 3.83 | 0.85 | 0.503 | 0.500 | 0.450 | 0.769 | |||
Learning Strategies (LS) | 3.75 | 0.92 | 0.375 | 0.473 | 0.430 | 0.504 | 0.817 | ||
Perceived ease of use (PEoU) | 3.52 | 0.96 | 0.468 | 0.408 | 0.500 | 0.456 | 0.449 | 0.772 | |
Perceived usefulness (PU) | 3.75 | 0.95 | 0.601 | 0.507 | 0.563 | 0.589 | 0.443 | 0.522 | 0.824 |
Table 3.
Measurement model.
Table 3.
Measurement model.
Construct | Indicator Item | Convergent Validity | Internal Consistency Reliability | ||
---|---|---|---|---|---|
Factor Loading | AVE | CA | CR | ||
>0.7 | >0.5 | >0.7 | >0.7 | ||
Behavioural Attitude | BA 1 | 0.854 | 0.689 | 0.774 | 0.869 |
BA 2 | 0.863 | ||||
BA 5 | 0.772 | ||||
Intention to use DGBL | BI 2 | 0.785 | 0.644 | 0.862 | 0.900 |
BI 3 | 0.827 | ||||
BI 4 | 0.835 | ||||
BI 6 | 0.784 | ||||
Perceived usefulness | PU 1 | 0.785 | 0.679 | 0.881 | 0.913 |
PU 2 | 0.870 | ||||
PU 3 | 0.847 | ||||
PU 4 | 0.850 | ||||
Perceived ease of use | PEoU 2 | 0.778 | 0.597 | 0.831 | 0.881 |
PEoU 3 | 0.740 | ||||
PEoU 5 | 0.806 | ||||
PEoU 6 | 0.775 | ||||
Learning strategies | LS 1 | 0.796 | 0.667 | 0.875 | 0.909 |
LS 2 | 0.868 | ||||
LS 3 | 0.807 | ||||
LS 4 | 0.833 | ||||
LS 5 | 0.792 | ||||
Learning opportunity | LO 1 | 0.758 | 0.591 | 0.827 | 0.878 |
LO 2 | 0.791 | ||||
LO 3 | 0.809 | ||||
LO 4 | 0.715 | ||||
LO 5 | 0.768 | ||||
Cognitive engagement | CE 1 | 0.836 | 0.641 | 0.815 | 0.877 |
CE 2 | 0.828 | ||||
CE 4 | 0.779 | ||||
CE 5 | 0.757 |
Table 4.
Total effects coefficients for the structural model.
Table 4.
Total effects coefficients for the structural model.
Hypothesis | Path | Path Coefficient |
t Value | p Value | VIF | f2 | Decision |
---|---|---|---|---|---|---|---|
H-1 | BA > BI | 0.600 | 10.676 | 0.000 | 1.632 | 0.537 | Supported |
H-2 | PU > BA | 0.491 | 8.841 | 0.000 | 1.373 | 0.250 | Supported |
H-3 | PEoU > BA | 0.338 | 3.585 | 0.000 | 1.373 | 0.071 | Supported |
H-4 | PEoU > PU | 0.257 | 4.060 | 0.000 | 1.697 | 0.079 | Supported |
H-5 | LS > PU | 0.122 | 1.129 | 0.259 | 2.133 | 0.002 | Rejected |
H-6 | LS > PEoU | 0.243 | 2.989 | 0.003 | 2.139 | 0.006 | Supported |
H-7 | LO > PU | 0.403 | 5.338 | 0.000 | 1.802 | 0.094 | Supported |
H-8 | LO > PEoU | 0.248 | 3.533 | 0.000 | 1.971 | 0.007 | Supported |
H-9 | CE > PU | 0.247 | 3.572 | 0.000 | 1.651 | 0.046 | Supported |
H-10 | CE > PEoU | 0.169 | 2.854 | 0.003 | 1.727 | 0.004 | Supported |
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