Journals / Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi / 2026 / Sayı: 80

The effect of instructional design supported by real-time information on students’ academic achievement and perceived learning

Gerçek zamanlı bilgiyle desteklenmiş öğretim tasarımının öğrencilerin akademik başarısına ve algılanan öğrenmelerine etkisi

Şahin Bayzan(Bilgi Teknolojileri ve İletişim Kurumu)0000-0002-5610-0006
Pages
248–273
Publication Date
2026-10-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that there was no significant difference between the two classes examined in terms of short-term academic achievement and learning gains.

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Objective & Hypothesis:The aim of this study is to examine the effect of an instructional design supported by pre-prepared digital information cards containing the lesson’s core learning outcomes, key concepts and critical explanations, presented immediately following the teacher’s explanation on the academic achievement and perceived learning levels of secondary school students.
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Key Findings & Data:The findings show that post-test scores were significantly higher than pre-test scores in both the experimental and control groups.
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Conclusion & Impact:The results show that there was no significant difference between the two classes examined in terms of short-term academic achievement and learning gains.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Gerçek zamanlı bilgi · dijital bilgi kartları · akademik başarı

Abstract

The aim of this study is to examine the effect of an instructional design supported by pre-prepared digital information cards containing the lesson’s core learning outcomes, key concepts and critical explanations, presented immediately following the teacher’s explanation on the academic achievement and perceived learning levels of secondary school students. The research was conducted using a pre-test/post-test controlled experimental design. The study group comprised a total of thiry-six secondary school students, with eighteen students in each of the experimental and control groups. In both groups, lessons were delivered by the same teacher using traditional oral instruction; in the experimental group, real-time digital information cards were used in addition to this instruction. Academic achievement was assessed using pre-tests and post-tests based on the same learning outcomes; perceived learning was assessed using a seven-item form adapted from a scale originally developed for university students to the secondary school level, which has shown initial evidence of validity regarding its unidimensional structure and exhibits high internal consistency. Data analysis utilised the Mann-Whitney U Test, the Wilcoxon Signed-Rank Test, the Independent Samples T-Test and Spearman’s Rank Correlation. The findings show that post-test scores were significantly higher than pre-test scores in both the experimental and control groups. However, no significant difference was found between the groups’ post-test and achievement scores. In contrast, the experimental group’s perceived learning scores were found to be significantly higher than those of the control group, and the difference was determined to have a large effect size. No significant relationship was found between the academic achievement indicators and perceived learning in either group. The results show that there was no significant difference between the two classes examined in terms of short-term academic achievement and learning gains. However, the perceived learning scores were higher in the class where teaching supported by digital flashcards was implemented. Due to the classroom-based nature of the study and differences in teaching durations, these results should not be interpreted as the independent effect of digital flashcards. Future research is recommended to integrate flashcards with active recall, student responses and feedback, and to re-examine their effects using larger samples, long-term implementations and delayed retention tests.

Özet

Bu araştırmanın amacı, öğretmen anlatımının hemen ardından sunulan, dersin temel kazanımlarını, anahtar kavramlarını ve kritik açıklamalarını içeren önceden hazırlanmış dijital bilgi kartlarıyla desteklenmiş öğretim tasarımının lise öğrencilerinin akademik başarıları ve algılanan öğrenme düzeyleri üzerindeki etkisini incelemektir. Araştırma, ön test-son test kontrol gruplu deneysel desenle yürütülmüştür. Çalışma grubunu deney ve kontrol gruplarında 18’er öğrenci olmak üzere toplam 36 lise öğrencisi oluşturmuştur. Her iki grupta dersler aynı öğretmen tarafından geleneksel sözlü anlatımla yürütülmüş; deney grubunda bu öğretime ek olarak gerçek zamanlı dijital bilgi kartları kullanılmıştır. Akademik başarı, aynı kazanımlara dayalı ön test ve son testlerle; algılanan öğrenme ise üniversite öğrencileri için geliştirilmiş bir ölçeğin lise düzeyine uyarlanması sonucunda elde edilen, tek faktörlü yapısına ilişkin ilk geçerlik kanıtları bulunan ve yüksek iç tutarlılık gösteren yedi maddelik form aracılığıyla değerlendirilmiştir. Verilerin analizinde Mann–Whitney U, Wilcoxon İşaretli Sıralar, bağımsız örneklemler t-testi ve Spearman sıra korelasyonu kullanılmıştır. Bulgular hem deney hem de kontrol grubunda son test puanlarının ön test puanlarından anlamlı düzeyde yüksek olduğunu göstermiştir. Bununla birlikte, grupların son test ve kazanım puanları arasında anlamlı bir fark bulunmamıştır. Buna karşılık deney grubunun algılanan öğrenme puanları kontrol grubundan anlamlı düzeyde yüksek bulunmuş ve farkın büyük etki büyüklüğüne sahip olduğu belirlenmiştir. Akademik başarı göstergeleri ile algılanan öğrenme arasında ise her iki grupta da anlamlı bir ilişki saptanmamıştır. Sonuçlar, incelenen iki sınıf arasında kısa süreli akademik başarı ve kazanım puanları bakımından anlamlı bir fark bulunmadığını, buna karşılık dijital bilgi kartlarıyla desteklenen öğretimin uygulandığı sınıfta algılanan öğrenme puanlarının daha yüksek olduğunu göstermiştir. Araştırmanın sınıf temelli yapısı ve öğretim sürelerindeki farklılık nedeniyle bu sonuçlar dijital bilgi kartlarının bağımsız etkisi olarak yorumlanmamalıdır. Gelecek araştırmalarda bilgi kartlarının aktif hatırlama, öğrenci yanıtı ve geri bildirimle bütünleştirilmesi; daha büyük örneklemler, uzun süreli uygulamalar ve gecikmeli kalıcılık testleriyle etkilerinin yeniden incelenmesi önerilmektedir.

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Cite This Article

Bayzan, Ş. (2026). The effect of instructional design supported by real-time information on students’ academic achievement and perceived learning. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, Sayı: 80, 248–273. https://doi.org/10.21764/maeuefd.1987858

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References (80)

  1. Akar, H. (2020). The effect of smart board use on academic achievement: A meta-analytical and thematic study. International Journal of Education in Mathematics, Science and Technology, 8(3), 261–273. https://doi.org/10.46328/ijemst.v8i3.908
  2. Aktas, E., & Yurt, S. U. (2017). Effects of Digital Story on Academic Achievement, Learning Motivation and Retention among University Students. International Journal of Higher Education, 6(1), 180. https://doi.org/10.5430/ijhe.v6n1p180
  3. Albayrak, E., Güngören, Ö. C., & Horzum, M. B. (2014). Adaptation of the Perceived Learning Scale into Turkish. Journal of the Faculty of Education, Ondokuz Mayıs University, 33(1), 1–14. https://doi.org/10.7822/omuefd.214860
  4. Alpizar, D., Adesope, O. O., & Wong, R. M. (2020). A meta-analysis of the signalling principle in multimedia learning environments. Educational Technology Research and Development, 68(5), 2095–2119. https://doi.org/10.1007/s11423-020-09748-7
  5. Alter, A. L., & Oppenheimer, D. M. (2009). Uniting the tribes of fluency to form a metacognitive nation. Personality and Social Psychology Review, 13(3), 219–235. https://doi.org/10.1177/1088868309341564
  6. Altman, D. G., & Bland, J. M. (1995). Statistics notes: Absence of evidence is not evidence of absence. BMJ, 311(7003), 485. https://doi.org/10.1136/bmj.311.7003.485
  7. Anderson, R., Anderson, R., Davis, P., Linnell, N., Prince, C., Razmov, V., & Videon, F. (2007). Classroom Presenter: Enhancing Interactive Education with Digital Ink. Computer, 40(9), 56–61. https://doi.org/10.1109/mc.2007.307
  8. Ayres, P. (2012). Can the isolated-elements strategy be improved by targeting points of high cognitive load for additional practice? Learning and Instruction, 23, 115–124. https://doi.org/10.1016/j.learninstruc.2012.08.002
  9. Batdi, V., Aslan, A., & Zhu, C. (2018). The effect of technology-supported teaching on students’ academic achievement: A combined meta-analytic and thematic study. International Journal of Learning Technology, 13(1), 44–60. https://doi.org/10.1504/IJLT.2018.091632
  10. Bayzan, Ş., & İnal, M. (2012). The use of real-time traffic information in determining vehicle routes for a navigation system. Engineering Sciences, 7(2), 488–496. https://izlik.org/JA35WZ38EF
  11. Bolkan, S. (2018). Facilitating student attention with multimedia presentations: examining the effects of segmented PowerPoint presentations on student learning. Communication Education, 68(1), 61–79. https://doi.org/10.1080/03634523.2018.1517895
  12. Cai, Z., Gui, Y., Mao, P., Wang, Z., Hao, X., Fan, X., & Tai, R. H. (2023). The effect of feedback on academic achievement in technology-rich learning environments (TREs): A meta-analytic review. Educational Research Review, 39, 100521. https://doi.org/10.1016/j.edurev.2023.100521
  13. Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2008). Spacing effects in learning. Psychological Science, 19(11), 1095–1102. https://doi.org/10.1111/j.1467-9280.2008.02209.x
  14. Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823
  15. Clearinghouse, W. W. (2022). Procedures and Standards Handbook, Version 5.0. U.S. Department of Education.
  16. Cumming, G. (2014). The new statistics: why and how. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966
  17. Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus the feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251–19257. https://doi.org/10.1073/pnas.1821936116
  18. Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266
  19. Edelsbrunner, P. A., Simonsmeier, B. A., & Schneider, M. (2025). Cronbach’s alpha of domain-specific knowledge tests before and after learning: A meta-analysis of published studies. Educational Psychology Review, 37, Article 4. https://doi.org/10.1007/s10648-024-09982-y
  20. Ginns, P. (2006). Integrating information: A meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction, 16(6), 511–525. https://doi.org/10.1016/j.learninstruc.2006.10.001
  21. Giurgiu, L. (2017). Microlearning: an evolving e-learning trend. Scientific Bulletin, 22(1), 18–23. https://doi.org/10.1515/bsaft-2017-0003
  22. Golding, J. M., Wasarhaley, N. E., & Fletcher, B. (2012). The use of flashcards in an introduction to psychology class. Teaching of Psychology, 39(3), 199–202. https://doi.org/10.1177/0098628312450436
  23. Hassanabadi, H., Robatjazi, E. S., & Savoji, A. P. (2011). Cognitive consequences of segmentation and modality methods in learning from instructional animations. Procedia – Social and Behavioural Sciences, 30, 1481–1487. https://doi.org/10.1016/j.sbspro.2011.10.287
  24. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
  25. Hung, H. (2015). Intentional vocabulary learning using digital flashcards. English Language Teaching, 8(10). https://doi.org/10.5539/elt.v8n10p107
  26. Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966–968. https://doi.org/10.1126/science.1152408
  27. Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137–152. https://doi.org/10.1037/a0028086
  28. Kester, L., Lehnen, C., Van Gerven, P. W., & Kirschner, P. A. (2006). Just-in-time presentation of schematic supportive information during the acquisition of cognitive skills. Computers in Human Behaviour, 22(1), 93–112. https://doi.org/10.1016/j.chb.2005.01.008
  29. Khong, H. K., & Kabilan, M. K. (2020). A theoretical model of micro-learning for second language instruction. Computer Assisted Language Learning, 35(7), 1483–1506. https://doi.org/10.1080/09588221.2020.1818786 Koriat, A. (1997). Monitoring one’s own knowledge during study: A cue-utilisation approach to judgements of learning. Journal of Experimental Psychology General, 126(4), 349–370. https://doi.org/10.1037/0096-3445.126.4.349
  30. Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9), 1297–1317. https://doi.org/10.1002/acp.1537
  31. Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. https://doi.org/10.3389/fpsyg.2013.00863
  32. Lin, M.-H., Chen, H.-C., & Liu, K.-S. (2017). A study of the effects of digital learning on learning motivation and learning outcomes. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3553–3564. https://doi.org/10.12973/eurasia.2017.00744a
  33. Maddox, G. B., Pyc, M. A., Kauffman, Z. S., Gatewood, J. D., & Schonhoff, A. M. (2018). Examining the contributions of desirable difficulty and reminding to the spacing effect. Memory & Cognition, 46(8), 1376–1388. https://doi.org/10.3758/s13421-018-0843-3
  34. Mayer, R. (2017). Using multimedia for e-learning. Journal of Computer Assisted Learning, 33(5), 403–423. https://doi.org/10.1111/jcal.12197
  35. Mayer, R. E. (2002). Multimedia learning. Psychology of Learning and Motivation, 41, 85–139. https://doi.org/10.1016/s0079-7421(02)80005-6
  36. Mayer, R. E. (2009). Multimedia learning. Cambridge University Press. https://assets.cambridge.org/97805217/35353/frontmatter/9780521735353_frontmatter.pdf
  37. Mayer, R. E. (2021). Evidence-based principles for how to design effective instructional videos. Journal of Applied Research in Memory and Cognition, 10(2), 229–240. https://doi.org/10.1016/j.jarmac.2021.03.007
  38. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. https://doi.org/10.1207/S15326985EP3801_6
  39. Mayer, R. E., & Pilegard, C. (2014). Principles for managing essential processing in multimedia learning: segmenting, pre-training, and modality principles. In Cambridge University Press eBooks (pp. 316–344). https://doi.org/10.1017/cbo9781139547369.016
  40. Mohammadi, M., Valizadeh, M., Zohdi Jalal, P., & Xodabande, I. (2024). University students’ academic vocabulary development through mobile-assisted learning: Exploring the impacts on receptive and productive knowledge. Heliyon, 10(7), Article e28103. https://doi.org/10.1016/j.heliyon.2024.e28103
  41. Nist, L., & Joseph, L. M. (2008). Effectiveness and efficiency of Flashcard Drill instructional methods on urban first-graders’ word recognition, acquisition, maintenance, and generalisation. School Psychology Review, 37(3), 294–308. https://doi.org/10.1080/02796015.2008.12087877
  42. Oldham, P. (1962). A note on the analysis of repeated measurements of the same subjects. Journal of Chronic Diseases, 15(10), 969–977. https://doi.org/10.1016/0021-9681(62)90116-9
  43. Paas, F., & Van Merriënboer, J. J. G. (2020). Cognitive-Load Theory: Methods to manage working memory load in the learning of complex tasks. Current Directions in Psychological Science, 29(4), 394–398. https://doi.org/10.1177/0963721420922183
  44. Paas, F., Van Gog, T., & Sweller, J. (2010). Cognitive Load Theory: New conceptualisations, specifications, and integrated research perspectives. Educational Psychology Review, 22(2), 115–121. https://doi.org/10.1007/s10648-010-9133-8
  45. Patall, E. A., Yates, N., Lee, J., Chen, M., Bhat, B. H., Lee, K., Beretvas, S. N., Lin, S., Yang, S. M., Jacobson, N. G., Harris, E., & Hanson, D. J. (2024). A meta-analysis of teachers’ provision of structure in the classroom and students’ academic competence beliefs, engagement, and achievement. Educational Psychologist, 59(1), 42–70. https://doi.org/10.1080/00461520.2023.2274104
  46. Persky, A. M., Lee, E., & Schlesselman, L. S. (2020). Perception of learning versus performance as outcome measures of educational research. American Journal of Pharmaceutical Education, 84(7), ajpe7782. https://doi.org/10.5688/ajpe7782
  47. Pham, X., Chen, G., Nguyen, T., & Hwang, W. (2016). Card-based design combined with spaced repetition: A new interface for displaying learning elements and improving active recall. Computers & Education, 98, 142–156. https://doi.org/10.1016/j.compedu.2016.03.014
  48. Polasek, R., & Javorcik, T. (2019). A microlearning approach to e-learning course creation and the reasons behind it. AIP Conference Proceedings, 2186, 060015. https://doi.org/10.1063/1.5137969
  49. Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12(1), 61–86. https://doi.org/10.1016/s0959-4752(01)00016-0
  50. Rhodes, M. G., & Castel, A. D. (2008). Memory predictions are influenced by perceptual information: Evidence for metacognitive illusions. Journal of Experimental Psychology General, 137(4), 615–625. https://doi.org/10.1037/a0013684
  51. Richland, L. E., Kornell, N., & Kao, L. S. (2009). The pretesting effect: Do unsuccessful retrieval attempts enhance learning? Journal of Experimental Psychology: Applied, 15(3), 243–257. https://doi.org/10.1037/a0016496
  52. Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20–27. https://doi.org/10.1016/j.tics.2010.09.003
  53. Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning. Psychological Science, 17(3), 249–255. https://doi.org/10.1111/j.1467-9280.2006.01693.x
  54. Rovai, A. P., Wighting, M. J., Baker, J. D., & Grooms, L. D. (2009). Development of an instrument to measure perceived cognitive, affective, and psychomotor learning in traditional and virtual classroom higher education settings. The Internet and Higher Education, 12(1), 7–13. https://doi.org/10.1016/j.iheduc.2008.10.002
  55. Saraç, H. (2017). The effect of interactive whiteboard use in the Turkish education system on students’ learning outcomes: A meta-analysis. Turkish Studies, 12(4), 445–470. https://doi.org/10.7827/TurkishStudies.11288
  56. Scheiter, K., & Eitel, A. (2014). Signals foster multimedia learning by supporting the integration of highlighted text and diagram elements. Learning and Instruction, 36, 11–26. https://doi.org/10.1016/j.learninstruc.2014.11.002 Schmidmaier, R., Ebersbach, R., Schiller, M., Hege, I., Holzer, M., & Fischer, M. R. (2011). Using electronic flashcards to promote learning in medical students: retesting versus restudying. Medical Education, 45(11), 1101–1110. https://doi.org/10.1111/j.1365-2923.2011.04043.x
  57. Schneider, S., Beege, M., Nebel, S., & Rey, G. D. (2018). A meta-analysis of how signalling affects learning with media. Educational Research Review, 23, 1–24. https://doi.org/10.1016/j.edurev.2017.11.001
  58. Schönbrodt, F. D., & Perugini, M. (2013). At what sample size do correlations stabilise? Journal of Research in Personality, 47(5), 609–612. https://doi.org/10.1016/j.jrp.2013.05.009
  59. Sitzmann, T., Ely, K., Brown, K. G., & Bauer, K. N. (2010). Self-assessment of knowledge: a cognitive learning or affective measure? Academy of Management Learning and Education, 9(2), 169–191. https://doi.org/10.5465/amle.9.2.zqr169
  60. Smith, C. D., & Scarf, D. (2017). Spacing repetitions over long timescales: A review and a reconsolidation explanation. Frontiers in Psychology, 8, 962. https://doi.org/10.3389/fpsyg.2017.00962
  61. Somyürek, S., Atasoy, B., & Özdemir, S. (2009). Board’s IQ: What makes a board smart? Computers & Education, 53(2), 368–374. https://doi.org/10.1016/j.compedu.2009.02.012
  62. Spanjers, I. a. E., Van Gog, T., & Van Merriënboer, J. J. G. (2010). A theoretical analysis of how segmentation of dynamic visualisations optimises students’ learning. Educational Psychology Review, 22(4), 411–423. https://doi.org/10.1007/s10648-010-9135-6
  63. Spanjers, I. a. E., Van Gog, T., & Van Merriënboer, J. J. G. (2011). Segmentation of worked examples: Effects on cognitive load and learning. Applied Cognitive Psychology, 26(3), 352–358. https://doi.org/10.1002/acp.1832
  64. Stockard, J., Wood, T. W., Coughlin, C., & Khoury, C. R. (2018). The Effectiveness of Direct Instruction Curricula: A Meta-Analysis of a Half Century of Research. Review of Educational Research, 88(4), 479–507. https://doi.org/10.3102/0034654317751919
  65. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
  66. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
  67. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. https://doi.org/10.1007/978-1-4419-8126-4
  68. Sweller, J., Van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/a:1022193728205
  69. Teymouri, R. (2024). Recent developments in mobile-assisted vocabulary learning: A mini review of published studies focusing on digital flashcards. Frontiers in Education, 9, Article 1496578. https://doi.org/10.3389/feduc.2024.1496578
  70. Titsworth, S., Mazer, J. P., Goodboy, A. K., Bolkan, S., & Myers, S. A. (2015). Two meta-analyses exploring the relationship between teacher clarity and student learning. Communication Education, 64(4), 385–418. https://doi.org/10.1080/03634523.2015.1041998
  71. Tsai, C., Shih, W., Hsieh, F., Chen, Y., & Lin, C. (2022). Applying the design-based learning model to foster undergraduates’ web design skills: the role of knowledge integration. International Journal of Educational Technology in Higher Education, 19(1). https://doi.org/10.1186/s41239-021-00308-4
  72. Tu, Y., & Gilthorpe, M. S. (2006). Revisiting the relationship between change and initial value: a review and evaluation. Statistics in Medicine, 26(2), 443–457. https://doi.org/10.1002/sim.2538
  73. Uttl, B. (2005). Measurement of individual differences. Psychological Science, 16(6), 460–467. https://doi.org/10.1111/j.0956-7976.2005.01557.x
  74. Van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive Load Theory and complex learning: recent developments and future directions. Educational Psychology Review, 17(2), 147–177. https://doi.org/10.1007/s10648-005-3951-0
  75. Van Merrienboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learner’s mind: Instructional design for complex learning. Educational Psychologist, 38(1), 5–13. https://doi.org/10.1207/s15326985ep3801_2
  76. Wekerle, C., Daumiller, M., & Kollar, I. (2022). Using digital technology to promote higher education learning: The importance of different learning activities and their relations to learning outcomes. Journal of Research on Technology in Education, 54(1), 1–17. https://doi.org/10.1080/15391523.2020.1799455
  77. Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10, Article 3087. https://doi.org/10.3389/fpsyg.2019.03087
  78. Xodabande, I., Atai, M. R., & Hashemi, M. R. (2024). Exploring the effectiveness of mobile-assisted learning with digital flashcards in enhancing long-term retention of technical vocabulary among university students. Journal of Computers in Education, 12(4), 1307–1332. https://doi.org/10.1007/s40692-024-00347-6
  79. Yeung, A. S. (1999). Cognitive Load and learner expertise: Split-Attention and redundancy effects in reading comprehension tasks with vocabulary definitions. The Journal of Experimental Education, 67(3), 197–217. https://doi.org/10.1080/00220979909598353
  80. Yüksel, H. G., Mercanoğlu, H. G., & Yılmaz, M. B. (2020). Digital flashcards versus wordlists for learning technical vocabulary. Computer Assisted Language Learning, 35(8), 2001–2017. https://doi.org/10.1080/09588221.2020.1854312