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Indonesian Journal of Education Research (IJoER)

Open Access Journal

Integrating Khan Academy as Video-Based Instruction: A Pre Experimental Action Research on Grade 7 Learners’ Achievement and Engagement

Eric Lascoña
Mindanao State University - Iligan Institute of Technology
Philippines
tagaytayeric131@gmail.com
Douglas A Salazar
Mindanao State University – Iligan Institute of Technology ROR
Philippines
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  • Purpose of the study: This action research examined whether integrating Khan Academy video lessons into Grade 7 mathematics classes would improve learners’ achievement and engagement levels, and documented students’ perceptions of the videos after a two-week classroom intervention.

    Methodology: A pre-experimental one-group pretest–posttest design was employed. Research instruments included a researcher-made 30-item Mathematics achievement test validated by three in-service teachers (reliability = 0.7742), a Mathematics Engagement Level Questionnaire using a 5-point Likert scale (Cronbach’s α = 0.857), and purposively selected Khan Academy video lessons. Data were analyzed using the Wilcoxon Signed-Rank Test, Spearman’s rho, and thematic analysis.

    Main Findings: Post-intervention achievement increased significantly, rising from a mean of 10.42 to 17.62, although most learners remained at the “Beginning” level. Engagement levels also improved after the intervention. The relationship between post-intervention achievement and engagement was weak and not statistically significant. Learners reported clearer understanding, increased enjoyment, and greater motivation, alongside some issues related to audio clarity and retention.

    Novelty/Originality of this study: This study contributes to educational research by demonstrating how short, teacher-guided integration of video-based instruction can simultaneously influence mathematics achievement and multidimensional learner engagement in secondary classrooms. By combining validated pre–post quantitative measures with learners’ thematic feedback, the study extends existing evidence on video-based learning by identifying both instructional benefits and implementation constraints (e.g., audio clarity, retention), thereby informing more effective and context-sensitive blending of video resources in mathematics instruction.

  • How to cite

    [1]
    E. Lascoña and D. A. Salazar, “Integrating Khan Academy as Video-Based Instruction: A Pre Experimental Action Research on Grade 7 Learners’ Achievement and Engagement”, Ind. Jou. Edu. Rsc, vol. 7, no. 2, pp. 144–153, Apr. 2026, doi: 10.37251/ijoer.v7i2.2402.

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    1. L. J. Rodríguez-Muñiz, D. Burón, Á. Aguilar-González, and L. Muñiz-Rodríguez, “Secondary mathematics teachers’ perception of their readiness for emergency remote teaching during the COVID-19 pandemic: A case study,” Education Sciences, vol. 11, no. 5, p. 228, 2021, doi: 10.3390/educsci11050228. DOI: https://doi.org/10.3390/educsci11050228
    2. K. Roth, Technology in Education: The Ongoing Debate of Access, Adequacy and Equity. 2020.
    3. M. Cabrol and E. Severin, “ICT to improve quality in education—A conceptual framework and indicators in the use of information communication technology for education (ICT4E),” in Assessing the Effects of ICT in Education: Indicators, Criteria and Benchmarks for International Comparisons, Paris, France: OECD, 2010, p. 83.
    4. C. Dziuban, C. R. Graham, P. D. Moskal, A. Norberg, and N. Sicilia, “Blended learning: the new normal and emerging technologies,” Int. J. Educ. Technol. Higher Educ., vol. 15, pp. 1–16, 2018, doi: 10.1186/s41239-017-0087-5. DOI: https://doi.org/10.1186/s41239-017-0087-5
    5. V. Moodley, “An investigation of learners’ performance in algebra from grades 9 to 11,” Ph.D. dissertation, Univ. of KwaZulu-Natal, Durban, South Africa, 2014.
    6. H. E. Vidergor and P. Ben-Amram, “Khan Academy effectiveness: The case of math secondary students’ perceptions,” Comput. Educ., vol. 157, Art. no. 103985, 2020, doi: 10.1016/j.compedu.2020.103985.
    7. D. Light and E. Pierson, “Increasing student engagement in math: The use of Khan Academy in Chilean classrooms,” Int. J. Educ. Develop. ICT, vol. 10, no. 2, pp. 103–119, 2014.
    8. R. Murphy, L. Gallagher, A. E. Krumm, J. Mislevy, and A. Hafter, Research on the Use of Khan Academy in Schools: Implementation Report. Menlo Park, CA, USA: SRI Int., 2014.
    9. H. L. Harmon, “Innovating a promising practice in high poverty rural school districts,” Rural Educ., vol. 41, no. 3, pp. 26–42, 2021. DOI: https://doi.org/10.35608/ruraled.v41i3.1018
    10. R. F. Kizilcec and G. L. Cohen, “Eight-minute self-regulation intervention raises educational attainment at scale in individualist but not collectivist cultures,” Proc. Natl. Acad. Sci. U.S.A., vol. 114, no. 17, pp. 4348–4353, 2017. DOI: https://doi.org/10.1073/pnas.1611898114
    11. J. F. Pane, B. A. Griffin, D. F. McCaffrey, and R. Karam, “Effectiveness of cognitive tutor algebra I at scale,” Educ. Eval. Policy Anal., vol. 36, no. 2, pp. 127–144, 2014, doi: 10.3102/0162373713507480. DOI: https://doi.org/10.3102/0162373713507480
    12. A. Barana and M. Marchisio, “An interactive learning environment to empower engagement in mathematics,” 2020. DOI: https://doi.org/10.55612/s-5002-045-014
    13. M. Alonazi, N. Beloff, and M. White, “Exploring determinants of m-government services: A study from the citizens’ perspective in Saudi Arabia,” in Proc. 2019 Federated Conf. Comput. Sci. Inf. Syst. (FedCSIS), 2019. DOI: https://doi.org/10.15439/2019F75
    14. J. M. Froiland and F. C. Worrell, “Intrinsic motivation, learning goals, engagement, and achievement in a diverse high school,” Psychol. Sch., vol. 53, no. 3, pp. 321–336, 2016, doi: 10.1002/pits.21901. DOI: https://doi.org/10.1002/pits.21901
    15. H. Ran, Y. Kim, and W. G. Secada, “A meta-analysis on the effects of technology’s functions and roles on students’ mathematics achievement in K–12 classrooms,” Journal of Computer Assisted Learning, 2021, doi: 10.1111/jcal.12611. DOI: https://doi.org/10.1111/jcal.12611
    16. D. A. Kulik, T. Wagner, S. V. Dmytrieva, G. Kosakowski, F. F. Hingerl, K. V. Chudnenko, and U. R. Berner, “GEM-Selektor geochemical modeling package: revised algorithm and GEMS3K numerical kernel for coupled simulation codes,” Comput. Geosci., vol. 17, pp. 1–24, 2013. DOI: https://doi.org/10.1007/s10596-012-9310-6
    17. F. Alrabai, “The effects of teachers’ in-class motivational intervention on learners’ EFL achievement,” Appl. Linguist., vol. 37, no. 3, pp. 307–333, 2016, doi: 10.1093/applin/amu021. DOI: https://doi.org/10.1093/applin/amu021
    18. F. D. Martin and D. U. Bolliger, “Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment,” Online Learning Journal, vol. 22, no. 1, pp. 205–222, 2018. DOI: https://doi.org/10.24059/olj.v22i1.1092
    19. R. Schindler, L. Burkholder, M. Morad, and C. Marsh, “Computer-based technology and student engagement: A critical review of the literature,” International Journal of Educational Technology in Higher Education, vol. 14, no. 25, 2017. DOI: https://doi.org/10.1186/s41239-017-0063-0
    20. J. A. Fredricks, A. L. Reschly, and S. L. Christenson, Eds., Handbook of Student Engagement Interventions: Working with Disengaged Students. Cambridge, MA, USA: Academic Press, 2019. DOI: https://doi.org/10.1016/B978-0-12-813413-9.00025-5
    21. T. K. F. Chiu, “Digital support for student engagement in blended learning based on self-determination theory,” Computers in Human Behavior, vol. 124, Art. no. 106909, 2021, doi: 10.1016/j.chb.2021.106909. DOI: https://doi.org/10.1016/j.chb.2021.106909
    22. N. Mercer and C. Howe, “Explaining the dialogic processes of teaching and learning: The value and potential of sociocultural theory,” Learning, Culture and Social Interaction, vol. 15, pp. 1–8, 2017.
    23. D. Zhang, Z. Fang, and M. Liao, “The impact of teacher’s pedagogical leadership on student achievement: The mediating role of teaching methods,” J. Prof. Capital Community, vol. 9, no. 4, pp. 398–430, 2024, doi: 10.1108/JPCC-11-2023-0074 DOI: https://doi.org/10.1108/JPCC-11-2023-0074
    24. T. Brafford, B. Harn, B. Clarke, C. T. Doabler, D. Kosty, and K. Scalise, “Implementation factors and their influence on student mathematics outcomes,” Learning Disabilities Research & Practice, vol. 38, no. 1, pp. 5–14, 2023, doi: 10.1111/ldrp.12298. DOI: https://doi.org/10.1111/ldrp.12298
    25. J. Roschelle, R. D. Pea, C. M. Hoadley, D. N. Gordin, and B. M. Means, “Changing how and what children learn in school with computer-based technologies,” The Future of Children, vol. 16, no. 2, pp. 76–101, 2016. DOI: https://doi.org/10.2307/1602690
    26. P. A. Kirschner and J. Sweller, “Why minimal guidance during instruction does not work: An analysis of the failure of constructivist teaching,” Educational Psychologist, vol. 41, no. 2, pp. 75–86, 2018. DOI: https://doi.org/10.1207/s15326985ep4102_1
    27. G. M. Mahuro and N. Hungi, “Parental participation improves student academic achievement: A case of Iganga and Mayuge districts in Uganda,” Cogent Educ., vol. 3, no. 1, Art. no. 1264170, 2016, doi: 10.1080/2331186X.2016.1264170. DOI: https://doi.org/10.1080/2331186X.2016.1264170
    28. J. Hattie, “Visible learning: A synthesis of over 800 meta-analyses relating to achievement,” Routledge, 2017.
    29. M. R. Reyes, M. A. Brackett, S. E. Rivers, N. A. Elbertson, and P. Salovey, “The interaction effects of program training, dosage, and implementation quality on targeted student outcomes for the RULER approach to social and emotional learning,” Sch. Psychol. Rev., vol. 41, no. 1, pp. 82–99, 2012. DOI: https://doi.org/10.1080/02796015.2012.12087377
    30. H. E. Vidergor and P. Ben-Amram, “Khan Academy effectiveness: The case of math secondary students’ perceptions,” Computers & Education, vol. 157, Art. no. 103985, 2020, doi: 10.1016/j.compedu.2020.103985. DOI: https://doi.org/10.1016/j.compedu.2020.103985
    31. L. Chen, J. Bae, and R. M. Battista, “The role of technology in enhancing student engagement in mathematics classrooms,” Computers & Education, vol. 165, Art. no. 104149, 2021, doi: 10.1016/j.compedu.2021.104149. DOI: https://doi.org/10.1016/j.compedu.2021.104149
    32. S. Bond, K. Bedenlier, O. Marín, and M. Händel, “Emergency remote teaching in higher education: Mapping the first global online semester,” Int. J. Educ. Technol. High. Educ., vol. 18, no. 50, 2021, doi: 10.1186/s41239-021-00282-x. DOI: https://doi.org/10.1186/s41239-021-00282-x
    33. J. A. Fredericks, P. C. Blumenfeld, and A. H. Paris, “School engagement: Potential of the concept, state of the evidence,” Rev. Educ. Res., vol. 74, no. 1, pp. 59–109, 2004.
    34. D. Hillmayr, L. Ziernwald, F. Reinhold, S. I. Hofer, and K. M. Reiss, “The potential of digital tools to enhance mathematics and science learning: A meta-analysis,” Computers & Education, vol. 153, Art. no. 103897, 2020, doi: 10.1016/j.compedu.2020.103897. DOI: https://doi.org/10.1016/j.compedu.2020.103897
    35. H. Lei, Y. Cui, and W. Zhou, “Relationships between student engagement and academic achievement: A meta-analysis,” Soc. Behav. Pers., vol. 46, no. 3, pp. 517–528, 2018, doi: 10.2224/sbp.7054. DOI: https://doi.org/10.2224/sbp.7054
    36. Y. Zhang, S. Russell, and S. Kelly, “Engagement, achievement, and teacher classroom practices in mathematics: Insights from TIMSS 2011 and PISA 2012,” Studies in Educational Evaluation, vol. 73, Art. no. 101146, 2022, doi: 10.1016/j.stueduc.2022.101146. DOI: https://doi.org/10.1016/j.stueduc.2022.101146
    37. J. A. Fredricks, P. C. Blumenfeld, and A. H. Paris, “School engagement: Potential of the concept, state of the evidence,” Review of Educational Research, vol. 74, no. 1, pp. 59–109, 2004. DOI: https://doi.org/10.3102/00346543074001059
    38. M. T. Wang and J. S. Eccles, “School context, achievement motivation, and academic engagement: A longitudinal study of school engagement using a multidimensional perspective,” Learn. Instr., vol. 28, pp. 12–23, 2013, doi: 10.1016/j.learninstruc.2013.04.002. DOI: https://doi.org/10.1016/j.learninstruc.2013.04.002
    39. K. L. Rueda-Gómez, L. J. Rodríguez-Muñiz, and L. Muñiz-Rodríguez, “Performance and mathematical self-concept in university students using Khan Academy,” Heliyon, vol. 9, no. 4, Art. no. e15441, 2023, doi: 10.1016/j.heliyon.2023.e15441. DOI: https://doi.org/10.1016/j.heliyon.2023.e15441
    40. Libraries Without Borders, Transforming Education – Intermediate Study. London, U.K.: Libraries Without Borders, 2014.