IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12265-d926719.html
   My bibliography  Save this article

Development and Application of an Intelligent Assessment System for Mathematics Learning Strategy among High School Students—Take Jianzha County as an Example

Author

Listed:
  • Guangming Wang

    (Faculty of Education, Tianjin Normal University, Tianjin 300387, China)

  • Xia Chen

    (Faculty of Education, Tianjin Normal University, Tianjin 300387, China)

  • Dongli Zhang

    (Faculty of Education, Tianjin Normal University, Tianjin 300387, China)

  • Yueyuan Kang

    (Faculty of Education, Tianjin Normal University, Tianjin 300387, China)

  • Fang Wang

    (Faculty of Education, Tianjin Normal University, Tianjin 300387, China)

  • Mingyu Su

    (School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China)

Abstract

To improve the quality of mathematics learning for high school students in economically disadvantaged areas, and promote education equity and sustainable development, this study developed the Mathematics Learning Strategies Intelligent Assessment and Strategy Implementation System by using artificial intelligence technology. The system fuses assessment scales, a set of norms, improvement strategies, and the intelligent assessment and strategy implementation program into an organic whole. The system can intelligently output all participants’ diagnosis results of the mathematics learning strategy in batches and automatically propose targeted improvement strategies for every participant. By applying the intelligent system to Jianzha County, Huangnan Tibetan Autonomous Prefecture, Qinghai Province, China, the results show that the mathematical learning strategies of high school students in Jianzha County were at a middle level; mathematical cognitive strategies and mathematical resource-management strategies need to be improved. The system’s effectiveness in practical applications was later tested via both quantitative methods, such as questionnaire surveys and testing, and qualitative methods, such as interviews, as well as evaluation by self and others. By intervening with participants according to the strategy implementation program provided by the system, it was found that their mathematics learning strategy level improved. The results of the study show that the system can accurately diagnose the level of mathematics learning strategies of high school students and that interventions based on the improvement measures can improve students’ mathematics learning strategy and mathematics achievements, indicating that the system is effective.

Suggested Citation

  • Guangming Wang & Xia Chen & Dongli Zhang & Yueyuan Kang & Fang Wang & Mingyu Su, 2022. "Development and Application of an Intelligent Assessment System for Mathematics Learning Strategy among High School Students—Take Jianzha County as an Example," Sustainability, MDPI, vol. 14(19), pages 1-36, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12265-:d:926719
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12265/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/19/12265/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cordero, Jose M. & Gil-Izquierdo, María, 2018. "The effect of teaching strategies on student achievement: An analysis using TALIS-PISA-link," Journal of Policy Modeling, Elsevier, vol. 40(6), pages 1313-1331.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Asadullah, M. Niaz & Perera, Liyanage Devangi H. & Xiao, Saizi, 2020. "Vietnam’s extraordinary performance in the PISA assessment: A cultural explanation of an education paradox," Journal of Policy Modeling, Elsevier, vol. 42(5), pages 913-932.
    2. Lagravinese, Raffaele & Liberati, Paolo & Resce, Giuliano, 2020. "The impact of economic, social and cultural conditions on educational attainments," Journal of Policy Modeling, Elsevier, vol. 42(1), pages 112-132.
    3. Bambang Budi Wiyono & Ach. Rasyad & Maisyaroh, 2021. "The Effect of Collaborative Supervision Approaches and Collegial Supervision Techniques on Teacher Intensity Using Performance-Based Learning," SAGE Open, , vol. 11(2), pages 21582440211, April.
    4. Ларина Г. С. & Капуза А. В., 2020. "Когнитивные Процессы В Преподавании: Связь С Достижениями Учащихся В Математике," Вопросы образования // Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 70-96.
    5. María Artemisa Sangermán Jiménez & Pedro Ponce, 2021. "Differentiated Teaching Based on Standardized Metrics Integrating Fuzzy Logic Type 2 Detection Theory: High School Case—PrepaTec, Mexico," Future Internet, MDPI, vol. 13(4), pages 1-19, April.
    6. Bhatnagar, Abhishek & Jaiswal, Animesh & Jain, Ansh & Bolia, Nomesh B., 2022. "An analysis of key indicators for enhancing school performance: Evidences from India," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    7. Agasisti, Tommaso & Longobardi, Sergio & Prete, Vincenzo & Russo, Felice, 2021. "The relevance of educational poverty in Europe: Determinants and remedies," Journal of Policy Modeling, Elsevier, vol. 43(3), pages 692-709.
    8. Galina Larina & Anastasia Kapuza, 2020. "Thinking Skills in Teaching Practices: Relationship with Students' Achievement in Mathematics," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 70-96.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12265-:d:926719. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.