由於在亞洲社會中，跨領域學習往往未受到足夠的重視，然而跨領域學習不僅可以加強學生在不同專業的能力，更可以幫助學生有新的思維角度，以提升未來在職場的競爭力，並且也能夠確認自己的興趣所在，故跨領域學習確為需要被重視的部分，但大部分學生卻會礙於成績考量抑或是對於未知領域的恐懼而怯於去修習跨領域課程，這是相當可惜的。
而在本研究中，學校體系的組成是較為複雜的，其由多個學院與多個科系所組成，因此，本研究旨在利用多層次勝算比架構（Multilevel Odds Ratio）去分析跨領域學習對於學生畢業走向是否存在影響，以此來探討學生跨領域學習的益處，也可以提供校方有較好的課程資源規劃，以幫助學生在未來有更好的競爭實力。
本研究資料來源為中央大學的校務資料，時間為2007年至2023年間，主要針對大學部學生直升中央大學碩士部的群體進行研究，共為4500筆，而使用的資料包含學生所修的課程、是否跨領域學習、升學走向是否為跨領域相關。

In many Asian societies, interdisciplinary learning often does not receive sufficient attention. However, such learning not only enhances students' competencies across multiple academic fields but also fosters new perspectives that strengthen their competitiveness in the job market. Moreover, it enables students to better identify and confirm their personal interests. Despite these benefits, many students are hesitant to enroll in interdisciplinary courses due to concerns about academic performance or fear of venturing into unfamiliar fields—an unfortunate situation that limits their potential development.
In the context of this study, the structure of the school system is relatively complex, comprising multiple colleges and departments. Therefore, this research aims to use Multilevel Odds Ratios to analyze whether interdisciplinary learning has an impact on students' post-graduation trajectories. Through this analysis, the study seeks to explore the benefits of interdisciplinary learning for students and provide insights that can help schools better plan course resources to enhance students' future competitiveness.
The data used in this study is sourced from the institutional records of National Central University, covering the period from 2007 to 2023. The focus is on undergraduate students who pursued a master’s degree at the same university, with a total of 4,500 records. The data includes information on courses taken, whether students engaged in interdisciplinary learning, and whether their graduate studies reflect a shift in academic disciplines.

[1] 網路資料：社企流。
取自 https://www.seinsights.asia/
[2] 網路資料：教育部全球資訊網。
取自 https://www.edu.tw/Default.aspx
[3] Inmon, William H., Building the Data Warehouse, Fourth Edition, Wiley Publishing, America, 2005.
[4] Dibouliya, A., “Review on: Modern Data Warehouse & how is it accelerating digital transformation”, International Journal of Advance Research, Ideas and Innovations in Technology, Vol 9(2), 2023.
[5] Fugkeaw, S., Suksai, P., Hak, L., “SSF-CDW: achieving scalable, secure, and fast OLAP query for encrypted cloud data warehouse”, Journal of Cloud Computing, Vol 13, 2024.
[6] Xiaofeng, W., & Michael, W. Kattan., “Cohort Studies: Design, Analysis, and Reporting”, CHEST, Vol 158, pp. 72-78, 2020.
[7] Mansoori, L., Kashi, Z., Khoshgoeian, A., Bahar, A., Moosazadeh, M., Ramezani, A., Soltani, S., “The Relationship Between Metabolic Syndrome with Physical Activity and Eating Habits: The Result of the Tabari Cohort Study”, Journal of Nursing and Midwifery Sciences, Vol 11, 2024.
[8] Aslanov, A.D., Kalibatov, R.M., Logvina, O.E., Gotyzhev, M.A., Kugotov, A.K., “Improvement of surgical intervention in laparoscopic cholecystectomy: Description and cohort study”, Journal of Medicinal and Pharmaceutical Chemistry Research, Vol 8, pp. 191-201, 2026.
[9] Louis, D., Nykiforuk, A., Chiu, A., Oberoi, S., Ruth, C., “Parental separation following preterm delivery in Canada: A population-based cohort study”, BMJ Paediatrics Open, Vol 8, 2024.
[10] Saxena, A., & Rajpoot, V., “A Comparative Analysis of Association Rule Mining Algorithms”, IOP Conference Series: Materials Science and Engineering, Vol 1099, March 2021.
[11] Manpreet, K., & Shivani, K., “Market Basket Analysis Identify the Changing Trends of Market Data Using Association Rule Mining”, Procedia Computer Science, Vol 85, pp. 78-85, 2016.
[12] Shuai, L., Jiaji, S., Yingyao, Z., Weidong, Z., Hu, L., Hongwen, X., Xiaoli, G., “Research and Application of Association Rule Mining Algorithm for Electric Power Equipment Manufacturing”, 2024 International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST), November 2024.
[13] Yingchun, S., “Research on Data Mining of Preschool Education Teachers’ Information Literacy Based on Association Rule Algorithm”, 2023 International Conference on Telecommunications, Electronics and Informatics (ICTEI), September 2023.
[14] Jiuyong, L., Thuc Duy, L., Lin, L., Jixue, L., Zhou, J., Bingyu, S., Saisai, M., “From Observational Studies to Causal Rule Mining”, ACM Transactions on Intelligent Systems and Technology, Vol 7(2), pp. 1-27, November 2015.
[15] Shahjahan, K., & Muhammad Ashraf, M., “Measuring Association between Two Categorical Variables Revisiting Risk Ratio and Odds Ratio”, International Journal of Statistical Sciences, Vol 20(2), pp. 159-170, 2020.
[16] Brian, C., Michael, L., Eric, C., Benjamin, M., John, W., “Effect size reporting among prominent health journals: a case study of odds ratios”, BMJ Evidence-Based Medicine, Vol 26, 2021.
[17] Karla, H., Jacqueline, Y., Monica, T., Samuel, I., Jessica, K., Jennifer, A., Brennan, C., Andrew, J., “Re-analysis of data from cluster randomised trials to explore the impact of model choice on estimates of odds ratios: study protocol”, Trials 25, Vol 25, 2024.
[18] Manolis, W., Georgia, K., “Educational Data Mining: A Foundational Overview”, Encyclopedia, Vol 4, pp. 1644-1664, 2024.
[19] Justyna, B., “The Problem of Zero Cells in the Analysis of Contingency Tables”, Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Vol 05, pp. 49-61, 2015.
[20] Baker, R.J., & Clarke, M.R.B., & Lane, P.W., “Zero entries in contingency tables”, Computational Statistics & Data Analysis, Vol 03, pp. 33-45, 1985.