Exploring learning analytics and motivated strategies for learning questionnaire (MSLQ) to understand pharmacy students’ learning profiles, motivation and strategies post-COVID
DOI:
https://doi.org/10.46542/pe.2023.231.656664Keywords:
Learning analytics, Learning strategy, Motivation, MSLQ, ProfileAbstract
Background: First-year pharmacy students experienced on-site education after three years of studying online in isolation.
Objectives: This study aimed to analyse newly enrolled first-year pharmacy students’ learning profiles using learning analytics from YouTube, and further understand their motivation and learning strategy during the transition period.
Method: Learning Analytics (LA) were retrieved from YouTube analytics on instructor-generated videos. Students’ motivation and learning strategies were acquired using the Motivated Strategies for Learning Questionnaire (MSLQ) with a seven-point Likert score distributed online using Google Forms. Data were analysed using SPSS, and interview sessions were conducted with some of the students.
Results: The LA showed most students referred to the instructor-generated video during study week. Students avoided the tutorial video with a view ratio lower than 1.0. This result correlated with the lower metacognitive mean compared to the cognitive level in the MSLQ analysis. Dependant on extrinsic components has increased their anxiety level. The peer learning scored higher than the help-seeking and was confirmed through interviews.
Conclusion: This study offers insights into students learning motivation and strategies. Well-designed instructional learning activities may help in improving their problem-solving skills to boost their motivation. The teacher-student relationship may need more effort to build.
References
Aluh, D. O., Abba, A., & Afosi, A. B. (2020). Prevalence and correlates of depression, anxiety, and stress among undergraduate pharmacy students in Nigeria. Pharmacy Education, 20(1), 236–248. https://doi.org/10.46542/pe.2020.201.236248
Berdida, D. J. E., & Grande, R. A. N. (2023). Nursing students’ nomophobia, social media use, attention, motivation, and academic performance: A structural equation modelling approach. Nurse Education in Practice, 70, 103645. https://doi.org/10.1016/j.nepr.2023.103645
Biwer, F., Egbrink, M. G. A., Aalten, P., & de Bruin, A. B. H. (2020). Fostering effective learning strategies in higher education–A mixed-methods study. Journal of Applied Research in Memory and Cognition, 9, 186–203. https://doi.org/10.1016/j.jarmac.2020.03.004
Choo, C. Y., Long, C. M., & Tan, C. S. (2022a). Fostering critical thinking in pharmaceutical chemistry: A cross-sectional study. Pharmacy Education, 22(1), 866–871. https://doi.org/10.46542/pe.2022.221.866871
Choo, C. Y., Fahrni, M. L., & Long, C. M. (2022b). Analysis of students’ cognitive presence and perception in a custom-designed virtual problem-based learning assignment. International Journal of Engineering and Technology (iJET), 17(22), 132–143. https://doi.org/10.3991/ijet.v17i22.32777
Cook, D. A., Thompson, W. G., & Thomas, K. G. (2011). The motivated strategies for learning questionnaire: Score validity among medicine residents. Medical Education, 45(12), 1230–40. https://doi.org/10.1111/j.1365-2923.2011.04077.x
Curtis, S. D., Guerci, J., Pullo, J., & Egelund, E. F. (2022). Motivational methods for first-year pharmacy students in professional practice skills laboratory. Pharmacy Education, 22(1), 533–539. https://doi.org/10.46542/pe.2022.221.533539
Esnaashari, S., Gardner, L. A., Arthanari, T. S., & Rehm, M. (2023). Unfolding self-regulated learning profiles of students: A longitudinal study. Journal of Computer Assisted Learning, 39(4), 1116–1131. https://doi.org/10.1111/jcal.12830
Feiz, P., Hooman, H. A., & Kooshki, S. (2013). Assessing the Motivated Strategies for Learning Questionnaire (MSLQ) in Iranian students: Construct validity and reliability. Procedia: Social & Behavioral Sciences, 84, 1820–1825. https://doi.org/10.1016/j.sbspro.2013.07.041
Galal, S., Vyas, D., Ndung’u, M., Wu, G., & Webber, M. (2023). Assessing learner engagement and the impact on academic performance within a virtual learning environment. Pharmacy, 11(1), 36. https://doi.org/10.3390/pharmacy11010036
Gehle, M., Trautner, M., & Schwinger, M. (2023). Motivational self-regulation in children with mild learning difficulties during middle childhood: Do they use motivational regulation strategies effectively? Journal of Applied Developmental Psychology, 84, 101487. https://doi.org/10.1016/j.appdev.2022.101487
Geng, X., Chen, L., Xu, Y., Ogata, H., Shimada, A., & Yamada, M. (2024). Learning behavioural patterns of students with varying performance in a high school mathematics course using an e-book system. Research and Practice in Technology Enhanced Learning (RPTEL), 19, 11. https://doi.org/10.58459/rptel.2024.19011
Gezgina, D. M., & Kurtça, T. T. (2023). Deep and surface learning approaches are related to fear of missing out on social networking sites: A latent profile analysis. Computers in Human Behavior, 149, 107962. https://doi.org/10.1016/j.chb.2023.107962
Güner, P., & Erbay, H. N. (2021). Metacognitive skills and problem-solving. International Journal of Research in Education and Science (IJRES), 7(3), 715–734. https://doi.org/10.46328/ijres.1594
Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2023). Supporting self-regulated learning with learning analytics interventions–A systematic literature review. Education and Information Technologies, 28, 3059–3088. https://doi.org/10.1007/s10639-022-11281-4
Heo, H., Bonk, C. J., & Doo, M. Y. (2022). Influences of depression, self-efficacy, and resource management on learning engagement in blended learning during COVID-19. The Internet and Higher Education, 54, 100856. https://doi.org/10.1016/j.iheduc.2022.100856
Karaoglan Yilmaz, F. G., & Yilmaz, R. (2021). Learning analytics as a metacognitive tool to influence learner transactional distance and motivation in an online learning environment. Innovations in Education and Teaching International, 58(5), 575–85. https://doi.org/10.1080/14703297.2020.1794928
Li, Q., Xu, D., Baker, R., Holton, A., & Warschauer, M. (2022). Can student-facing analytics improve online students’ efforts and success by affecting how they explain the cause of past performance? Computers & Education, 185, 104517. https://doi.org/10.1016/j.compedu.2022.104517
Moilanen, K. L. (2007). The adolescent self-regulatory inventory: The development and validation of a questionnaire of short-term and long-term self-regulation. Journal of Youth and Adolescence, 36(6), 835–848. https://doi.org/10.1007/s10964-006-9107-9
Nelson, T. O., & Narens, L. (1990). Metamemory: A theoretical framework and new findings. Psychology of Learning and Motivation–Advances in Research and Theory, 26, 125–173. https://doi.org/10.1016/S0079-7421(08)60053-5
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press.
Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational Psychology Measurement, 53, 801–813. https://doi.org/10.1177/0013164493053003024
Soemantri, D., Mccoll, G., & Dodds, A. (2018). Measuring medical students’ reflection on their learning: Modification and validation of the motivated strategies for learning questionnaire (MSLQ). BMC Medical Education, 18, 274. https://doi.org/10.1186/s12909-018-1384-y
Stoian, C. E., Fărcașiu M. A., Dragomir G. M., & Gherheș, V. (2022). Transition from online to face-to-face education after Covid-19: The benefits of online education from students’ perspective. Sustainability, 14(19), 12812. https://doi.org/10.3390/su141912812
Tuominen, H., Niemivirta, M., Lonka, K., & Salmela-Aro, K. (2020). Motivation across a transition: Changes in achievement goal orientations and academic well-being from elementary to secondary school. Learning and Individual Differences, 79, 101854. https://doi.org/10.1016/j.lindif.2020.101854
Waldeyer, J., Fleischer, J., Wirth, J., & Leutner, D. (2020). Validating the resource-Management Inventory (ReMI): Testing measurement invariance and predicting academic achievement in a sample of first-year university students. European Journal of Psychological Assessment, 36(5), 777–786. https://doi.org/10.1027/1015-5759/a000557
Zeidner, M., & Stoeger, H. (2019). Self-regulated learning (SRL): A guide for the perplexed. High Ability Studies, 30, 9–51. https://doi.org/10.1080/13598139.2019.1589369
Zimmerman, B. J. (2011). Motivational sources and outcome of self-regulated learning and performance. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 49–64), Routledge.