Paper title: The Impact of Pre-Lecture Quizzes on Students’ Performance in Blended Learning
Published in: Issue 2, (Vol. 13) / 2019Download
Publishing date: 2019-12-16
Pages: 25-31
Author(s): OSMAN Cristina-Claudia
Abstract. Testing is a powerful mechanism to enhance the learning process. This study analyses the impact of pre-lecture quizzes on students’ performance. Based on quizzes results, 2 groups are created. A series of indices are used in order to decide the number of clusters for each group. The analysis of each cluster reveals details related to the association between the results obtained by students on quizzes and their performance on course. Post-hoc tests like Tukey’s test, Levene test, and Games Howel test are used to investigate the variances between students’ performance based on quizzes’ results
Keywords: Students’ Performance, Pre-lecture Quizzes, Blended Learning, Case Study, Moodle
References:1. M. Prensky (2001), Digital Natives, Digital Immigrants Part 1, On the Horizon, Vol. 9, No. 5, pp. 1-6.
2. E. Rapetti, The knowledge society between “smart devices” and “digital learners”: A pedagogical‐anthropological reflection about the implications of dominant rhetoric in eLearning field, in Proceedings of the Red-conference: Rethinking Education in the KSnowledge Society, Zurich, 2011, pp. 236-253.
3. J. Watson (2008), Blended Learning: The Convergence of Online and Face-to-Face Education. Promising Practices in Online Learning. North American Council for Online Learning, pp. 1-18.
4. C.J. Bonk and C.R. Graham. The Handbook of Blended Learning: Global Perspectives, Local Designs, John Wiley & Sons, 2012.
5. M. Driscoll (2002), Blended learning: Let’s get beyond the hype. Online. Available:, Accessed August 8, 2019.
6. H. Singh, and C. Reed (2001), A white paper: Achieving success with blended learning, Centra software, 1, pp. 1-11, Online. Available:, Accessed August 8, 2019.
7. A.P. Rovai, and H. Jordan (2004). Blended Learning and Sense of Community: A Comparative Analysis with Traditional and Fully Online Graduate Courses. The International Review of Research in Open and Distributed Learning, Vol. 5, No. 2, pp. 1-13, Online. Available:, Accessed August 8, 2019.
8. Y. Wang, X. Han, and J. Yang (2015), Revisiting the Blended Learning Literature: Using a Complex Adaptive Systems Framework, Educational Technology & Society, Vol. 18, No. 2, pp. 380–393.
9. B. Collis, and J. Moonen (2002), Flexible learning in a digital world. Open Learning: The Journal of Open, Distance and e-Learning, Vol. 17, No. 3, pp. 217-230.
10. R. Mason (2004), Telecommunications media, Using communications media in open and flexible learning, RoutledgeFalmer, pp. 11-23.
11. R.R. Martin, and K. Srikameswaran (1974), Correlation between frequent testing and student performance, Journal of Chemical Education, Vol. 51, No. 7, pp. 485-486.
12. J.E. Anderson (1984), Frequency of quizzes in a behavioural science course: An attempt to increase medical student study behavior, Teaching of Psychology, Vol. 11, No. 1, p. 34.
13. A.G. Brink (2013), The impact of pre-and post-lecture quizzes on performance in Intermediate Accounting II, Issues in Accounting Education, Vol. 28, No. 3, pp. 461-485.
14. F.C. Leeming (2009), The exam-a-day procedure improves performance in psychology classes, Teaching of Psychology, Vol. 29, No. 3, pp. 210–212.
15. N. Kling , D. McCorkle , C. Miller and J. Reardon (2005), The Impact of Testing Frequency on Student Performance in a Marketing Course, Journal of Education for Business, Vol. 81, No. 2, pp. 67-72.
16. L. Umek, D. Keržič, N. Tomaževič, and A. Aristovnik, A. The impact of Moodle quizzes on student performance: the case of a statistics course, in Proc. 8th Internat. Conf. Information, Communication Technologies in Education, Chania, Crete, Greece, 2018, pp. 69-76.
17. G.A. Negin (1981), The Effects of Test Frequency in a First-Year Torts Course, Journal of Legal Education, Vol. 31, pp. 673-676.
18. H.L. Roediger III, and J.D. Karpicke (2006), The power of testing memory: Basic research and implications for educational practice, Perspectives on psychological science, Vol. 1, No. 3, pp. 181-210.
19. L.K. Michaelsen, L. Dee Fink and W.E. Watson (1994), Pre-instructional minitests: An efficient solution to the problem of covering content, Journal of Management Education, Vol. 18, No. 1, pp. 32-44.
20. D. Cohen, and I. Sasson (2016), Online quizzes in a virtual learning environment as a tool for formative assessment, Journal of Technology and Science Education, Vol. 6, No. 3, pp. 188-208.
21. M.W.Alexander, J.E. Bartlett, A.D. Truell and K. Ouwenga (2001), Testing in a computer technology course: An investigation of equivalency in performance between online and paper and pencil methods, Journal of Career and Technical Education, Vol. 18, No. 1, pp. 69-80.
22. M.L. Still, and J.D. Still (2015), Contrasting Traditional In-Class Exams with Frequent Online Testing, Journal of Teaching and Learning with Technology, Vol. 4, No. 2, pp. 30-40.
23. V. Gholami, and M.M. Moghaddam (2013), The effect of weekly quizzes on students' final achievement score, International Journal of Modern Education and Computer Science, Vol. 5, No. 1, pp. 36-41.
24. M. North, and R.Richardson (2018). Analysis of effectiveness of optional versus mandatory quizzes on final comprehensive examinations performance, International Management Review, Vol. 14, No. 1, pp. 38-67.
25. R. S. J. D. Baker (2010), Data mining for education, International encyclopedia of education, Vol. 7, no. 3, pp. 112-118.
26. M. Pechenizkiy, N. Trcka, E. Vasilyeva, W.M.P. van der Aalst, P. de Bra (2009) Process mining online assessment data, in Proc.2nd Internat. Conf. Educational Data Mining, Cordoba, Spain, pp. 279-288.
27. P. Mukala, J.C.A.M. Buijs, and W.M.P. van der Aalst (2015), Exploring students’ learning behaviour in MOOCs using process mining techniques, BPM reports, Vol. 1510, pp. 179-196.
28. C. Romero, P.G. Espejo, A. Zafra, J.R. Romero, and S. Ventura (2013), Web usage mining for predicting final marks of students that use Moodle courses, Computer Applications in Engineering Education, Vol. 21, No. 1, pp. 135-146.
29. M. Charrad, N. Ghazzali, V. Boiteau and A. Niknafs, NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set, Journal of Statistical Software, Vol. 61, No. 6, pp. 1-36.
30. T. Calinski, J. Harabasz (1974), A Dendrite Method for Cluster Analysis, Communications in Statistics - Theory and Methods, Vol. 3, No. 1, pp. 1-27.
31. W.S. Sarle (1983), Cubic Clustering Criterion, SAS Technical Report A-108, SAS Institute Inc.
32. L.J. Hubert, J.R. Levin (1976), A General Statistical Framework for Assessing Categorical Clustering in Free Recall, Psychological Bulletin, Vol. 83, No. 6, pp. 1072-1080.
33. P. Rousseeuw (1987). Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis, Journal of Computational and Applied Mathematics, Vol. 20, pp. 53-65.
34. R.O. Duda, P.E. Hart (1973), Pattern Classification and Scene Analysis, John Wiley & Sons, New York.
35. E.M.L. Beale (1969), Cluster Analysis, Scientific Control Systems, London.
36. D.A. Ratkowsky, G.N. Lance (1978), A Criterion for Determining the Number of Groups in a Classification. Australian Computer Journal, Vol. 10, No. 3, pp. 115-117.
37. R. Tibshirani, G. Walther, T. Hastie (2001), Estimating the Number of Clusters in a Data Set Via the Gap Statistic, Journal of the Royal Statistical Society B, Vol. 63, No. 2, pp. 411-423.
38. J.O. McClainand V.R. Rao (1975), CLUSTISZ: A Program to Test for The Quality of Clustering of a Set of Objects, Journal of Marketing Research, Vol. 12, No. 4, pp. 456-460.
39. H.J. Hubert, P. Arabie (1985), Comparing Partitions, Journal of Classification, Vol. 2, No. 1, pp. 193-218.
40. L. Lebart, A. Morineau, M. Piron (2000). Statistique Exploratoire Multidimensionnelle. Dunod,Paris.
41. W.J. Krzanowski and Y.T. Lai (1988), A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering. Biometrics, Vol. 44, No. 1, pp. 23-34.
42. J.A. Hartigan (1975), Clustering Algorithms. John Wiley & Sons, New York.
43. A.J. Scott and M.J. Symons (1971), Clustering Methods Based on Likelihood Ratio Criteria, Biometrics, Vol. 27, No. 2, pp. 387-397.
44. G.W. Milligan and M.C. Cooper (1985), An Examination of Procedures for Determining the Number of Clusters in a Data Set. Psychometrika, Vol. 50, No. 2, pp. 159-179.
45. G.H. Ball, D.J. Hall (1965), ISODATA: A Novel Method of Data Analysis and Pattern Classification, Stanford Research Institute, Menlo Park.

Back to the journal content
Creative Commons License
This article is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License.
Home | Editorial Board | Author info | Archive | Contact
Copyright JACSM 2007-2020