Main Article Content
Abstract
Arising from the widespread use of and access to the internet, digital tools have rapidly increased over the past two decades. In a plural and contentious field, digital tools are anything that can aid a student in their learning process or beyond. This ranges from an internet-based referencing tool, desktop publishing software, online tutorials, simulations, computer-based writing programs, and a comprehensive suite of tools. Despite this, effect sizes for learning using digital tools do not favor schools' high adoption of such tools, swayed by important cognitive psychology data: students learn best when the material is tailored to their cognitive level, interactive, and when feedback is maximized. Digital tools can allow this learning to occur, but only if applied in a way consistent with the cited factors. Currently, schools' high use of digital tools could be associated with the 'just do it' approach to implementation, where benefits are expected to be immediate and intuitive without teacher training or severe consideration of tool design. This has resulted in teachers and students sometimes overvaluing tools with no sound cognitive foundation or evidence base. This criticism validates that learning should be based on 'what works' and not political/ideological preference. Despite this, many educational researchers from an education technology background agree that digital tools have transformative power to learning if used to reflect sound pedagogical design. This disparity between tool advocates and the broader educational community has fueled the current debate of whether digital tools are good/wrong for student learning and their uptake by schools. This review focuses on the impact of modern digital tools on students, with the scope being all associated aspects of schooling and student learning.
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References
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References
Adeel, S., Daniel, A. D., & Botelho, A. (2023). The effect of entrepreneurship education on the determinants of entrepreneurial behavior among higher education students: A multi-group analysis. Journal of Innovation and Knowledge, 8(1). https://doi.org/10.1016/J.JIK.2023.100324
Alshebami, A. S., Al-Jubari, I., Alyoussef, I. Y., & Raza, M. (2020). Entrepreneurial education as a predictor of community college of Abqaiq students' entrepreneurial intention. Management Science Letters, 10(15), 3605–3612. https://doi.org/10.5267/J.MSL.2020.6.033
Alzahrani, N. A., & Abdullah, M. A. (2019). Student Engagement Effectiveness In E-Learning System. Bioscience Biotechnology Research Communications, 12(1), 208–218. https://doi.org/10.21786/BBRC/12.1/24
Badri, R., & Hachicha, N. (2019). Entrepreneurship education and its impact on students' intention to start up: A sample case study of students from two Tunisian universities. International Journal of Management Education, 17(2), 182–190. https://doi.org/10.1016/j.ijme.2019.02.004
Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: Testing the linkages. Research in Higher Education, 47(1), 1–32. https://doi.org/10.1007/S11162-005-8150-9
Cashman, L., Sabates, R., & Alcott, B. (2021). Parental involvement in low-achieving children's learning: The role of household wealth in rural India. International Journal of Educational Research, 105. https://doi.org/10.1016/j.ijer.2020.101701
Çetinkaya, A., & Baykan, Ö. K. (2020a). Prediction of middle school students' programming talent using artificial neural networks. Engineering Science and Technology, an International Journal, 23(6), 1301–1307. https://doi.org/10.1016/j.jestch.2020.07.005
Çetinkaya, A., & Baykan, Ö. K. (2020b). Prediction of middle school students' programming talent using artificial neural networks. Engineering Science and Technology, an International Journal, 23(6), 1301–1307. https://doi.org/10.1016/j.jestch.2020.07.005
Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(JULY). https://doi.org/10.3389/FPSYG.2019.01652
Chapman, L. (2006). Improving patient care through work-based learning. Nursing Standard (Royal College of Nursing (Great Britain) : 1987), 20(41), 41–45. https://doi.org/10.7748/NS2006.06.20.41.41.C6549
Conrad, D. (2022). From the Trenches: Students' Reflections on Their Online Doctoral Programs. Opening the Online Door to Academe, 87–91. https://doi.org/10.1163/9789004521216_008
Del Valle, R., & Duffy, T. M. (2009). Online learning: Learner characteristics and their approaches to managing learning. Instructional Science, 37(2), 129–149. https://doi.org/10.1007/S11251-007-9039-0
Gallant, M., Majumdar, S., & Varadarajan, D. (2010). Outlook of female students towards entrepreneurship: An analysis of a selection of business students in Dubai. Education, Business and Society: Contemporary Middle Eastern Issues, 3(3), 218–230. https://doi.org/10.1108/17537981011070127
Gyasewaa, F., Tinyogtaa Adama, D., Nanyele, S., Love Obo Amissah, A., & Cimagala Samantha Dieckmann Professor Zakir Aliyev, B. (2023a). Family Factors and Students' Academic Performance of Students in Senior High Schools. Scholars Journal of Science and Technology, 4(4), 72–89. https://doi.org/10.53075/Ijmsirq/454346432
Gyasewaa, F., Tinyogtaa Adama, D., Nanyele, S., Love Obo Amissah, A., & Cimagala Samantha Dieckmann Professor Zakir Aliyev, B. (2023b). Family Factors and Students' Academic Performance of Students in Senior High Schools. Scholars Journal of Science and Technology, 4(4), 72–89. https://doi.org/10.53075/Ijmsirq/454346432
Heo, M., & Toomey, N. (2020). Learning with multimedia: The effects of gender, type of multimedia learning resources, and spatial ability. Computers and Education, 146. https://doi.org/10.1016/j.compedu.2019.103747
Hidayat, C., Rohyana, A., & Lengkana, A. S. (2022). Students' Perceptions Toward Practical Online Learning in Physical Education: A Case Study. Kinestetik : Jurnal Ilmiah Pendidikan Jasmani, 6(2), 279–288. https://doi.org/10.33369/jk.v6i2.21658
Huang, J., Li, Y. F., & Xie, M. (2015). An empirical analysis of data preprocessing for machine learning-based software cost estimation. Information and Software Technology, 67, 108–127. https://doi.org/10.1016/j.infsof.2015.07.004
Jan, S. (2018). Investigating the Relationship between Students' Digital Literacy and Their Attitude towards Using ICT. International Journal of Educational Technology, 5(2), 26–34.
Krause, K. L., & Coates, H. (2008). Students' engagement in first-year university. Assessment and Evaluation in Higher Education, 33(5), 493–505. https://doi.org/10.1080/02602930701698892
Major, D. (2016). Models of work-based learning, examples and reflections. Journal of Work-Applied Management, 8(1), 17–28. https://doi.org/10.1108/JWAM-03-2016-0003
Noetel, M., Griffith, S., Delaney, O., Harris, N. R., Sanders, T., Parker, P., del Pozo Cruz, B., & Lonsdale, C. (2022). Multimedia Design for Learning: An Overview of Reviews With Meta-Meta-Analysis. Review of Educational Research, 92(3), 413–454. https://doi.org/10.3102/00346543211052329
Nygren, T., & Guath, M. (2021). Students Evaluating and Corroborating Digital News. Scandinavian Journal of Educational Research, 1–17.
Otache, I., Umar, K., Audu, Y., & Onalo, U. (2021). The effects of entrepreneurship education on students' entrepreneurial intentions: A longitudinal approach. Education and Training, 63(7–8), 967–991. https://doi.org/10.1108/ET-01-2019-0005
Ouragini, I., & Lakhal, L. (2024). The Effect of Entrepreneurial Marketing Education on The Determinants of Students' Entrepreneurial Intention. International Journal of Management Education, 22(1). https://doi.org/10.1016/j.ijme.2023.100903
Overwien, A., Jahnke, L., & Leker, J. (2024). Can entrepreneurship education activities promote students' entrepreneurial intention? International Journal of Management Education, 22(1). https://doi.org/10.1016/j.ijme.2023.100928
Razali, S. N. A. M., Rusiman, M. S., Gan, W. S., & Arbin, N. (2018). The Impact of Time Management on Students' Academic Achievement. Journal of Physics: Conference Series, 995(1). https://doi.org/10.1088/1742-6596/995/1/012042
Sakurai, Y., Parpala, A., Pyhältö, K., & Lindblom-Ylänne, S. (2016). Engagement in learning: a comparison between Asian and European international university students. Compare, 46(1), 24–47. https://doi.org/10.1080/03057925.2013.866837
Samuel, N., Onasanya, S. A., & Yusuf, M. O. (2019). Engagement, Learning Styles and Challenges of Learning in the Digital Era among Nigerian Secondary School Students. International Journal of Education and Development Using Information and Communication Technology, 15(4), 35–43.
Santo, L. D., Peña-Jimenez, M., Canzan, F., Saiani, L., & Battistelli, A. (2022). The emotional side of the e-learning among nursing students: The role of the affective correlates on e-learning satisfaction. Nurse Education Today, 110. https://doi.org/10.1016/j.nedt.2022.105268
Schmid, R., & Petko, D. (2019). Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students? Computers & Education, 136, 75–86.
Siah, C. J. R., Huang, C. M., Poon, Y. S. R., & Koh, S. L. S. (2022). Nursing students' perceptions of online learning and its impact on knowledge level. Nurse Education Today, 112. https://doi.org/10.1016/j.nedt.2022.105327
Souitaris, V., Zerbinati, S., & Al-Laham, A. (2007). Do entrepreneurship programmes raise entrepreneurial intention of science and engineering students? The effect of learning, inspiration and resources. Journal of Business Venturing, 22(4), 566–591. https://doi.org/10.1016/j.jbusvent.2006.05.002
Tatipang, D. P., Manuas, M. J., Wuntu, C. N., Rorintulus, O. A., & Lengkoan, F. (2022). EFL Students' Perceptions of the Effective English Teacher Characteristics. Jurnal Pendidikan Bahasa Inggris Undiksha, 10(1), 23–30. https://doi.org/10.23887/jpbi.v10i1.4
Terkan, R. (2014). Importance of Creative Advertising and Marketing According to University Students' Perspective. International Review of Management and Marketing, 4(3), 239–246. www.econjournals.com
Wei, H., Sun, J., Shan, W., Xiao, W., Wang, B., Ma, X., Hu, W., Wang, X., & Xia, Y. (2022). Environmental chemical exposure dynamics and machine learning-based prediction of diabetes mellitus. Science of the Total Environment, 806. https://doi.org/10.1016/j.scitotenv.2021.150674