Home⇛International Journal of Multidisciplinary: Applied Business and Education Research⇛vol. 5 no. 3 (2024)

Level of Acceptability of Computer-Assisted Assessment (CAA) and Student’s Academic Performance in Key Stage 2 of Ilalim Elementary School for The School Year 2022-2023

Michael G. Albino | Jesse S. Agoja, Jr.

Discipline: Education

 

Abstract:

In this research study, the researcher aimed to determine the level of acceptability of computer-assisted assessment and learners’ academic performance of 127 Key Stage 2 Learners of Ilalim Elementary School for the School Year 2022-2023. A descriptive survey instrument was used to determine the level of acceptability adapted from the computer-based assessment acceptance model (CBAAM) and was modified by the researcher. Data revealed that the use of computer-assisted assessment of the Key Stage 2 learners was Moderately Acceptable with a mean of 4.01, indicating that the application purposively used it to improve their knowledge and skills. The learners gained an Outstanding academic performance as reflected in their general weighted average from the four grading periods. Significant differences were found in the level of acceptability of the use of computer-assisted assessment on the profile variables of the learner-respondents. Moreover, the results showed a positive correlation between the level of acceptability of the use of computer-assisted assessment on the learner’s academic performance. Based on the gathered data from the survey, computer-assisted assessment provides the learners with a positive experience when using the application through its friendly user interface. Teachers may continue to use the computer-assisted assessment application to motivate their learners to increase their engagement in learning their subject. It is recommended that teachers may further enhance the delivery of questions in the CAA application to improve learners’ academic performance in the future.



References:

  1. Abu Al Aish, A., & Love, S. (2013). Factors Influencing Students’ Acceptance of M-Learning: An Investigation in Higher Education. The International Review of Research in Open and Distance Learning, 14, 83–107. https://doi.org/10.19173/irrodl.v14i5.1631
  2. Ajzen, I. (1985). From Intentions to Actions : A Theory of Planned Behavior.
  3. Alderson, J. C., Brunfaut, T., & Harding, L. (2019). Towards a Theory of Diagnosis in Second and Foreign Language Assessment: Insights from Professional Practice Across Diverse Fields. Applied Linguistics, 36(2), 236–260. https://doi.org/10.1093/applin/amt046
  4. Bahari, A. (2021). Computer-assisted language proficiency assessment tools and strategies. Open Learning, 36(1), 61–87. https://doi.org/10.1080/02680513.2020.1726738
  5. Baozhou, L., & Liang, S. (2020). Use of Video Clips in a Virtual Learning Environment of Accounting Information Systems Class—A Case Study. Open Journal of Accounting, 2020(October), 107–109. http://www.scirp.org/journal/PaperInformation.aspx?PaperID=38277
  6. Bull, J., & McKenna, C. (2004). A Blueprint for Computer-Assisted Assessment. https://doi.org/10.4324/9780203464687
  7. Chalmers, D., & McCausland, W. D. (2002). Computer-assisted Assessment (from The Handbook for Economics Lecturers). The Handbook of Economics Lecturers, 2–12. http://www.economicsnetwork.ac.uk/handbook/printable/caa_v5.pdf
  8. Chong, S. W. (2021). Reconsidering student feedback literacy from an ecological perspective. Assessment & Evaluation in Higher Education, 46(1), 92–104. https://doi.org/10.1080/02602938.2020.1730765
  9. Conole, G., & Warburton, B. (2018). A review of computer-assisted assessment. Alt-J, 13(1), 17–31. https://doi.org/10.1080/0968776042000339772
  10. Croft, A. C., Danson, M., Dawson, B. R., & Ward, J. P. (2021). Experiences of using computer assisted assessment in engineering mathematics. Computers & Education, 37(1), 53–66. https://doi.org/https://doi.org/10.1016/S0360-1315(01)00034-3
  11. Csapó, B., & Molnár, G. (2019). Online diagnostic assessment in support of personalized teaching and learning: The eDia system. Frontiers in Psychology, 10 (JULY). https://doi.org/10.3389/fpsyg.2019.01522
  12. Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Sloan School of Management, Massachusetts Institute of Technology.
  13. DepEd. (2022). RM No. 152 s. 2022 Training of Trainers on the Computer-Based Assessment Relative to Programme For International Student Assessment (PSA).pdf.
  14. Deutsch, T., Herrmann, K., Frese, T., & Sandholzer, H. (2012). Implementing computer-based assessment - A web-based mock examination changes attitudes. Computers & Education, 58, 1068–1075. https://doi.org/10.1016/j.compedu.2011.11.013
  15. Dijkstra, A. J. (2019). Getting to grips with. European Journal of Lipid Science and Technology, 111(6), 530–536.
  16. Ehiwario, J. C., & Aghamie, S. O. (2021). Effects of Computer Assisted Instructional Software on Senior Secoundary School Students ’ Achievement and Retention in Geometry in Delta State. The Journal of the Mathematical Association of Nigeria, 46(1), 48–57. https://doi.org/10.21275/SR21610193320
  17. Esfandiari, R., & Meihami, H. (2023). Iranian EFL Teachers’ Challenges and Solutions in Using Computer-Assisted Assessment: Exploring EFL Teachers’ Experiential Evidence (pp. 313–329). https://doi.org/10.1007/978-981-99-0514-0_19
  18. Fishbein, M., Jaccard, J., Davidson, A. R., Ajzen, I., & Loken, B. (1980). Predicting and understanding family planning behaviors.
  19. Ghilay, Y. (2019). Computer Assisted Assessment (CAA) in Higher Education: Multi-text and Quantitative Courses. Journal of Online Higher Education, 3(1), 13–34. https://ezproxy.ufs.ac.za/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edsdoj&AN=edsdoj.3eeb9fa5fba4e7e8d3e6d630c2dda6f&site=eds-live&scope=site
  20. Ghilay, Y., & Ghilay, R. (2021). Student Evaluation in Higher Education: a Comparison Between Computer Assisted Assessment and Traditional Evaluation. I-Manager’s Journal of Educational Technology, 9(2), 8–16. https://doi.org/10.26634/jet.9.2.1942
  21. Hattie, J., & Timperley, H. (2017). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
  22. Kashwani, G. (2019). A Critical Review on the Sustainable Development Future. Journal of Geoscience and Environment Protection, 07(03), 1–11. https://doi.org/10.4236/gep.2019.73001
  23. Knight, P. (2021). A Briefing on Key Concepts: Formative and Summative, Criterion and Norm-referenced Assessment. In Learning and Teaching Support Network (LTSN) Generic Centre (NV-, Vol. 7, Issue 7, pp. 1–31). Learning and Teaching Support Network. https://doi.org/LK - https://worldcat.org/title/166464741
  24. Kumar, J. A., Muniandy, B., & Yahaya, W. A. J. W. (2021). The Relationship Between Emotional Intelligence and Students’ Attitude Towards Computers: A Study on Polytechnic Engineering Students. International Journal of Modern Education and Computer Science, 4(9), 14–22. https://doi.org/10.5815/ijmecs.2012.09.02
  25. Lai, C. (2013). A framework for developing self-directed technology use for language learning. Language Learning and Technology, 17(2), 100–122.
  26. Landry, B., Griffeth, R., & Hartman, S. (2006). Measuring student perceptions of blackboard using the Technology Acceptance Model. Decision Sciences Journal of Innovative Education, 4, 87–99. https://doi.org/10.1111/j.1540-4609.2006.00103.x
  27. LeJeune, L. M., & Lemons, C. J. (2021). The Effect of Computer-Assisted Instruction on Challenging Behavior and Academic Engagement. Journal of Positive Behavior Interventions, 23(2), 118–129. https://doi.org/10.1177/1098300720929680
  28. Li, X., & Luo, H. (2023). Research on the Teaching and Learning Strategies in College English Reading Curriculum from the Perspective of Second Language Acquisition. OALib, 10(03), 1–10. https://doi.org/10.4236/oalib.1109889
  29. Lopez, S. J. (Ed), & Snyder, C. R. (Ed). (2019). Oxford h andbook of positive psychology, 2nd ed. In S. J. Lopez & C. R. Snyder (Eds.), Oxford handbook of positive psychology, 2nd ed. (pp. xxxi, 709–xxxi, 709). Oxford University Press.
  30. Manjunatha, N. (2019). Descriptive research. Journal of Emerging Technologies and Innovative Research (JETIR), 6(6), 863–867. https://doi.org/10.1093/fampra/8.1.92
  31. Maqableh, M., Masa’deh, R. M. T., & Mohammed, A. B. (2015). The Acceptance and Use of Computer Based Assessment in Higher Education. Journal of Software Engineering and Applications, 08(10), 557–574. https://doi.org/10.4236/jsea.2015.810053
  32. Marriott, P., & Lau, A. (2021). The use of on-line summative assessment in an undergraduate financial accounting course. Journal of Accounting Education, 26(2), 73–90. https://doi.org/https://doi.org/10.1016/j.jaccedu.2008.02.001
  33. Marshall, S. (2010). Change, technology and higher education: Are universities capable of organisational change? Australasian Journal of Educational Technology, 26(8), 179–192. https://doi.org/10.14742/ajet.1018
  34. Moon, J.-W., & Kim, Y.-G. (2021). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217–230. https://doi.org/https://doi.org/10.1016/S0378-7206(00)00061-6
  35. Murillo Montes de Oca, A., & Nistor, N. (2020). Non-significant intention–behavior effects in educational technology acceptance: A case of competing cognitive scripts? Computers in Human Behavior, 34, 333–338. https://doi.org/https://doi.org/10.1016/j.chb.2014.01.026
  36. Noorbehbahani, F., & Kardan, A. A. (2021). The automatic assessment of free text answers using a modified BLEU algorithm. Computers & Education, 56(2), 337–345. https://doi.org/https://doi.org/10.1016/j.compedu.2010.07.013
  37. Padilla-Meléndez, Del Aguila-Obra, A. R., & Garrido-Moreno, A. (2021). Padilla-Meléndez, A., Del Aguila-Obra, A. R., & Garrido-Moreno, A. (2012). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education.
  38. Pourdana, N. (2022). Impacts of computer-assisted diagnostic assessment on sustainability of L2 learners’ collaborative writing improvement and their engagement modes. Asian-Pacific Journal of Second and Foreign Language Education, 7(1), 11. https://doi.org/10.1186/s40862-022-00139-4
  39. QAA. (2018). University of Bath quality audit report.
  40. Ritter, O. N. (2018). Integration of educational technology for the purposes of differentiated instruction in secondary STEM education. The University of Tennessee.
  41. Rodrigues, F., & Oliveira, P. (2019). A system for formative assessment and monitoring of students’ progress. Computers & Education, 76, 30–41. https://doi.org/https://doi.org/10.1016/j.compedu.2014.03.001
  42. Romero, E. J. (2018). AACSB Accreditation: Addressing Faculty Concerns. Academy of Management Learning & Education, 7(2), 245–255. http://www.jstor.org/stable/40214540
  43. Saha, S. K., & Gupta, R. (2020). Adopting computer-assisted assessment in evaluation of handwritten answer books: An experimental study. Education and Information Technologies, 25(6), 4845–4860. https://doi.org/10.1007/s10639-020-10192-6
  44. Salac, D. M. V. (2018). PRESENT: An Android-Based Class Attendance Monitoring System Using Face Recognition Technology. International Journal of Computing Sciences Research, 2(3), 102–115. https://doi.org/10.25147/ijcsr.2017.001.1.28
  45. Schoen-Phelan, B., & Keegan, B. (2018). Case Study on Performance and Acceptance of Computer-Aided Assessment. International Journal for E-Learning Security, 6(1), 482–487. https://doi.org/10.20533/ijels.2046.4568.2016.0061
  46. Sclater, N. (2018). Open Research Online The Open University ’ s repository of research publications Using evaluation to inform the development of a user- focused assessment engine Conference Item. August.
  47. Seale, J. (n.d.). Using CAA to support student. Technology.
  48. Seale, J. K. (2021). Developing e-learning experiences and practices: the importance of context. Alt-J, 16(1), 1–3. https://doi.org/10.1080/09687760701850141
  49. Shee, D. Y., & Wang, Y.-S. (2018). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education, 50(3), 894–905. https://doi.org/https://doi.org/10.1016/j.compedu.2006.09.005
  50. Sillat, L. H., Tammets, K., & Laanpere, M. (2021). Digital competence assessment methods in higher education: A systematic literature review. Education Sciences, 11(8). https://doi.org/10.3390/educsci11080402    
  51. Sobremisana, V. S., & Aragon, R. C. (2016). Computer-Based Assessment : Its Effects On Students ’ Performance And Computer-Based Assessment : Its Effects On Students ’ Performance And Attitude. The International Journal Research Publication’s, 5(9), 10–18.
  52. Stefan, P., Pfandler, M., Kullmann, A., Eck, U., Koch, A., Mehren, C., von der Heide, A., Weidert, S., Fürmetz, J., Euler, E., Lazarovici, M., Navab, N., & Weigl, M. (2023). Computer-assisted simulated workplace-based assessment in surgery: application of the universal framework of intraoperative performance within a mixed-reality simulation. BMJ Surgery, Interventions, & Health Technologies, 5(1), e000135. https://doi.org/10.1136/bmjsit-2022-000135
  53. Tareef, A. Bin. (2021). the Effects of Computer-Assisted Learning on the Achievement and Problem Solving Skills of the Educational Statistics Students. European Scientific Journal, 10(28), 1857–7881.
  54. Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
  55. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. In Computers & Education (Vol. 52, pp. 302–312). Elsevier Science. https://doi.org/10.1016/j.compedu.2008.08.006
  56. Terzis, V., & Economides, A. A. (2021). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032–1044. https://doi.org/https://doi.org/10.1016/j.compedu.2010.11.017 
  57. Terzis, V., Moridis, C. N., & Economides, A. A. (2019). How student’s personality traits affect Computer Based Assessment Acceptance: Integrating BFI with CBAAM. Computers in Human Behavior, 28(5), 1985–1996. https://doi.org/https://doi.org/10.1016/j.chb.2012.05.019
  58. Thelwall, M. (2020). Computer-based assessment: a versatile educational tool. Computers & Education, 34(1), 37–49. https://doi.org/https://doi.org/10.1016/S0360-1315(99)00037-8
  59. Tshibalo, A. (2018a). The potential impact of computer-aided assessment technology in higher education. South African Journal of Higher Education, 21(6), 684–693. https://doi.org/10.4314/sajhe.v21i6.25738
  60. Tshibalo, A. (2018b). The Potential impact of computer-aided assessment technology in higher education. South African Journal of Higher Education, 21. https://doi.org/10.4314/sajhe.v21i6.25738
  61. Undi, V. A., & Hashim, H. (2021). The Demands of 21st Century Learning: A Study on Primary School Teachers’ Attitudes towards Using ICT in English as a Second Language (ESL) Classrooms. Creative Education, 12(07), 1666–1678. https://doi.org/10.4236/ce.2021.127127
  62. Van Raaij, E., & Schepers, J. (2018). The acceptance and use of virtual learning environment in China. Computers & Education, 50, 838–852. https://doi.org/10.1016/j.compedu.2006.09.001
  63. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  64. Wang, C. H., Harrison, J., Cardullo, V., & Lin, X. (2018). Exploring the relationship among international students’ english self-efficacy, using english to learn self-efficacy, and academic self-efficacy. Journal of International Students, 8(1), 233–250. https://doi.org/10.5281/zenodo.1134299
  65. Wang, J., & Ondago, O. (2021). Optimization of Computer-Assisted Vocabulary Assessment System in International Chinese Language Teaching. Computer-Aided Design and Applications, 18, 106–117. https://doi.org/10.14733/cadaps.2021.S4.106-117
  66. Winke, P., & Isbell, D. (2017). Computer-Assisted Language Assessment (pp. 313–325). https://doi.org/10.1007/978-3-319-02237-6_25
  67. Wu, W.-L., Hsu, Y., Yang, Q.-F., Chen, J.-J., & Jong, M. S.-Y. (2023). Effects of the self-regulated strategy within the context of spherical video-based virtual reality on students’ learning performances in an art history class. Interactive Learning Environments, 31(4), 2244–2267. https://doi.org/10.1080/10494820.2021.1878231
  68. YARON, G. (2018). Video-Based Learning of Quantitative Courses in Higher Education. I-Manager’s Journal of Educational Technology, 15(2), 16. https://doi.org/10.26634/jet.15.2.14302

Tools


Copyright © 2024  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic