• search hit 1 of 1
Back to Result List

Secondary school students’ motivation profiles for physics learning

  • For efficient and effective pedagogical interventions to address Uganda's alarmingly poor performance in Physics, it is vital to understand students' motivation patterns for Physics learning. Latent profile analysis (LPA)-a person-centred approach-can be used to investigate these motivation patterns. Using a three-step approach to LPA, we sought to answer the following research questions: RQ1, which profiles of secondary school students exist with regards to their motivation for Physics learning; RQ2, are there differences in students' cognitive learning strategies in the identified profiles; and RQ3, does students' gender, attitudes, and individual interest predict membership in these profiles? The sample comprised 934 Grade 9 students from eight secondary schools in Uganda. Data were collected using standardised questionnaires. Six motivational profiles were identified: (i) low-quantity motivation profile (101 students; 10.8%); (ii) moderate-quantity motivation profile (246 students; 26.3%); (iii) high-quantity motivation profileFor efficient and effective pedagogical interventions to address Uganda's alarmingly poor performance in Physics, it is vital to understand students' motivation patterns for Physics learning. Latent profile analysis (LPA)-a person-centred approach-can be used to investigate these motivation patterns. Using a three-step approach to LPA, we sought to answer the following research questions: RQ1, which profiles of secondary school students exist with regards to their motivation for Physics learning; RQ2, are there differences in students' cognitive learning strategies in the identified profiles; and RQ3, does students' gender, attitudes, and individual interest predict membership in these profiles? The sample comprised 934 Grade 9 students from eight secondary schools in Uganda. Data were collected using standardised questionnaires. Six motivational profiles were identified: (i) low-quantity motivation profile (101 students; 10.8%); (ii) moderate-quantity motivation profile (246 students; 26.3%); (iii) high-quantity motivation profile (365 students; 39.1%); (iv) primarily intrinsically motivated profile (60 students, 6.4%); (v) mostly extrinsically motivated profile (88 students, 9.4%); and (vi) grade-introjected profile (74 students, 7.9%). Low-quantity and grade-introjected motivated students mostly used surface learning strategies whilst the high-quantity and primarily intrinsically motivated students used deep learning strategies. Lastly, unlike gender, individual interest and students' attitudes towards Physics learning predicted profile membership. Teachers should provide an interesting autonomous Physics classroom climate and give students clear instructions in self-reliant behaviours that promote intrinsic motivation.show moreshow less

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details:Diana KwarikundaORCiDGND, Ulrich SchiefeleORCiDGND, Joseph SsenyongaORCiD, Charles Magoba MuwongeORCiD
DOI:https://doi.org/10.1080/18117295.2021.1956720
ISSN:1028-8457
Title of parent work (English):African journal of research in mathematics, science and technology education : official journal of the Southern African Association for Research in Mathematics, Science and Technology Education
Subtitle (English):Relations with cognitive learning strategies, gender, attitudes and individual interest
Publisher:Routledge, Taylor & Francis Group
Place of publishing:Abingdon
Publication type:Article
Language:English
Date of first publication:2021/05/04
Publication year:2021
Release date:2023/11/30
Tag:Motivation profiles; latent profile analysis; physics learning
Volume:25
Issue:2
Number of pages:14
First page:197
Last Page:210
Funding institution:Katholischer Akademischer Auslander-Dienst (KAAD)
Organizational units:Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Peer review:Referiert
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.