Reflection of connectivism in medical education and learning motivation during COVID-19

  1. Jun Xin Lee
  2. Ahmad Hathim Ahmad Azman
  3. Jing Yi Ng
  4. Noor Akmal Shareela Ismail

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Abstract

The COVID-19 pandemic has not only affected the global healthcare and economy but threatened the world of education altogether. Malaysia is not spared from this pandemic as all universities were forced to close and initiate online learning with the implementation of Movement Control Order since mid-March 2020.The abrupt shift from conventional medical education to fully virtual learning definitely deserves a reflection on how it affects the learning motivation among medical students. Hence, this is the first study that compares the effect of digital learning on learning motivation among medical students in Universiti Kebangsaan Malaysia (UKM) prior to and during the COVID-19 pandemic. A modified Students Motivation towards Science Learning (SMTSL) was used to assess the learning motivation of UKM medical students throughout Year 1-5. The number of students that use digital learning during COVID-19 is significantly higher compared to before COVID-19 (p<0.05). However, there is no significant difference (p=0.872) in learning motivation among medical students before and during COVID-19 crisis. Higher frequency in digital learning usage frequency does not exert a great impact on learning motivation. Reflections from each participant were collated to justify the current situation. This could be due to motivation coming from the very choice to pursue medicine as a doctor, which is mainly influenced by intrinsic motivation, and ability to adapt in difficult situations. Thus, medical educators should be creative in enhancing extrinsic motivation by making use of digital learning as a platform so that medical students are able to independently fish for information in the vast pool of digital information and apply in actual medical practice in the future for life-long learning.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.07.20147918: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: Procedure: After obtaining Universiti Kebangsaan Malaysia (UKM) Ethics approval (FF-2020-037), a set of questionnaires including information sheet and consent form were distributed via Google Forms through social media platform, WhatsApp.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Results were recorded using Statistical Package for Social Science (SPSS) Version 22 by the IBM Corporation, New York, United States.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study was conducted with a few potential limitations. Since this was a cross-sectional study, we could not accurately elicit the direct causal relationship between digital learning and learning motivation among medical students. Additional longitudinal studies are recommended in the future to further explore the relationship between digital learning and learning motivation and feasibly on students’ learning performance. Next, since only UKM medical students are involved in this study, there are limitations to the representativeness and generalizability of the findings to another setting in other medical faculties. Additionally, the likelihood of respondents giving socially desirable responses as participants may answer the questionnaire positively based on what they perceive to be expected of them since our study used self-reported data.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.07.07.20147918v1 on medRxiv