This work is licensed under the Creative Commons Attribution 4.0 International License.
Ahmed, Amer. (2024). 1. Predictive Model for Student’s Academic Performance Using Machine Learning Techniques. International Journal for Science Technology and EngineeringSearch in Google Scholar
Alejandro, A., Vaisman., Florencia, Besteiro., Maximiliano, Valverde. (2019). 5. Modelling and Querying Star and Snowflake Warehouses Using Graph Databases.Search in Google Scholar
Aman, F., Rauf, A., Ali, R., Iqbal, F., & Khan, M. (2019). A predictive model for predicting students’ academic performance.Search in Google Scholar
Amani, Khalifa., Fatma, BenSaid., Yessine, Hadj, Kacem., Zouhaier, Jridi. (2023). 4. At-Risk Students Identification based on Machine Learning Approach: A Case Study of Computer Science Bachelor Student in Tunisia.Search in Google Scholar
Amita, Dhankhar., Kamna, Solanki., Sandeep, Dalal., Omdev. (2021). Predicting Students Performance Using Educational Data Mining and Learning Analytics: A Systematic Literature Review.Search in Google Scholar
Andres, Gonzalez-Nucamendi., Julieta, Noguez., Luis, Neri., V., Robledo-Rella., R., M., G., García-Castelán. (2023). 2. Predictive analytics study to determine undergraduate students at risk of dropout. Frontiers in Education,Search in Google Scholar
Asiah, M., Zulkarnaen, K. N., Safaai, D., & Shahbudin, H. M. (2019). A review on predictive modeling technique for student academic performance monitoring.Search in Google Scholar
Akid, H., Frey, G., Ayed, M., & Lachiche, N. (2022). Performance of NoSQL graph implementations of star vs. snowflake schemas. IEEE AccessSearch in Google Scholar
Duarte, R., Ramos-Pires, A., & others. (2014). Identifying at-risk students in higher education.Search in Google Scholar
E., N., M., Nimy., Moeketsi, Mosia., Colin, Chibaya. (2023). 5. Identifying At-Risk Students for Early Intervention – a Probabilistic Machine Learning Approach. Social Science Research Network,Search in Google Scholar
Gaftandzhieva, S., Hussain, S., Hilcenko, S., Doneva, R., & Boykova, K. (2023). Data-driven decision making in higher education institutions: state-of-play.Search in Google Scholar
Gupta, A., Garg, D., & Kumar, P. (2022). An ensembling model for early identification of at-risk students in higher education.Search in Google Scholar
He, J., Bailey, J., Rubinstein, B., & Zhang, R. (2015). Identifying at-risk students in massive open online courses.Search in Google Scholar
Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D., & Long, Q. (2018). Predicting academic performance by considering student heterogeneity.Search in Google Scholar
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. (2018). Predicting academic performance: a systematic literature review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education.Search in Google Scholar
Iqbal, M., Mustafa, G., Sarwar, N., Wajid, S., Nasir, J., & Siddque, S. (2019). A Review of Star Schema and Snowflakes Schema.Search in Google Scholar
Isreal, M., Ogundele., Olutosin, Taiwo., Asegunloluwa, Eunice, Babalola., Olumide, C, Ayeni. (2024). 4. Prediction of Student Academic Performance Based on Machine Learning Model.Search in Google Scholar
J., B., Osborne., A.-S., Lang. (2023). 3. Predictive Identification of At-Risk Students: Using Learning Management System Data.Search in Google Scholar
Kam, Cheong, Li., Billy, Wong., Hon, Tung, Chan. (2023). 1. Prediction of At-Risk Students Using Learning Analytics: A Literature Review. Communications in computer and information scienceSearch in Google Scholar
Kam, Cheong, Li., Billy, Wong., Hon, Tung, Chan. (2023). 1. Prediction of At-Risk Students Using Learning Analytics: A Literature Review. Communications in computer and information scienceSearch in Google Scholar
Kam, Cheong, Li., Billy, Wong., Hon, Tung, Chan. (2023). Prediction of At-Risk Students Using Learning Analytics: A Literature Review. Communications in computer and information science.Search in Google Scholar
Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.Search in Google Scholar
M., Zafar, Iqbal., Ghulam, Mustafa., Nadeem, Sarwar., Syed, Hamza, Wajid., Junaid, Nasir., Shaista, Siddque. (2019). 3. A Review of Star Schema and Snowflakes Schema.Search in Google Scholar
Manish, A., Bhide., Srinivas, K., Mittapalli., Sriram, Padmanabhan. (2011). 4. Star and snowflake schemas in extract, transform, load processes.Search in Google Scholar
Ofori, R., & Charlton, J. P. (2002). A path model of factors influencing the academic performance of nursing students.Search in Google Scholar
Osborne, J.B., & Lang, A.S.I.D. (2023). Predictive Identification of At-Risk Students: Using Learning Management System Data.Search in Google Scholar
Ouatik, F., Erritali, M., Ouatik, F., & Jourhmane, M. (2022). Predicting Student Success Using Big Data and Machine Learning Algorithms. Int. J. Emerg. Technol. Learn.Search in Google Scholar
Pardo, A., Mirriahi, N., Martinez-Maldonado, R., Yacef, K., & Kay, J. (2016). Generating actionable predictive models of academic performance.Search in Google Scholar
Pei, B., & Xing, W. (2022). An interpretable pipeline for identifying at-risk students. Journal of Educational Computing Research, 60(6), 743-763.Search in Google Scholar
Preet, Kamal., Sachin, Ahuja. (2017). A Review on Prediction of Academic Performance of Students At-Risk Using Data Mining Techniques.Search in Google Scholar
Shen, Fei-fei. (2007). An explorer of the student crisis forecast and support system in higher education based on data warehouse. Journal of Chaohu College.Search in Google Scholar
Teng, Y., Zhang, J., & Sun, T. (2022). Data‐driven decision‐making model based on artificial intelligence in higher education system of colleges and universities.Search in Google Scholar
Tucker, L., & McKnight, O. (2019). Assessing the Validity of College Success Indicators for the At-Risk Student: Toward Developing a Best-Practice Model. Journal of College Student Retention: Research, Theory & PracticeSearch in Google Scholar
Vasconcelos, A.N., Freires, L.A., Loureto, G.D.L., & others. (2023). Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students.Search in Google Scholar
What is a data warehouse? | Definition, components, architecture | SAP. (n.d.). SAP.https://www.sap.com/products/technology-platform/datasphere/what-is-a-data-warehouse.htmlSearch in Google Scholar
Xun, Cheng., Peter, Schneider. (2014). 2. Star and snowflake join query performance.Search in Google Scholar
Yohannes, Kurniawan., Erwin, Halim. (2013). Use data warehouse and data mining to predict student academic performance in schools: A case study (perspective application and benefits).Search in Google Scholar