IJIET 2026 Vol.16(5): 1196-1209
doi: 10.18178/ijiet.2026.16.5.2588
doi: 10.18178/ijiet.2026.16.5.2588
Predicting the Effectiveness of Scientific Inquiry in Educational Technology in Moroccan Secondary Schools: A KNN-Based Analysis through Observations and Interviews
El Mostapha Bouhamid1,*, Said Chakiri1, Mohamed Yazidi2, Mohammed El-moudden1,
Omar Amahmid2,3, and
Houda Itouni1
1. Geosciences and Natural Resources Laboratory, Faculty of Sciences, Ibn Tofaïl University, Kenitra, Morocco
2. Multidisciplinary Research Laboratory in Didactics, Education, And Training, Department of Life and Earth Sciences, Regional Center for Education and Training Professions, Marrakech-Safi, Main Headquarters, Marrakech, Morocco
3. Laboratory of Natural Resources and Sustainable Development, Ibn Tofail University, Kenitra, Morocco
Email: elmostapha.bouhamid@uit.ac.ma (E.M.B.); chakiri@uit.ac.ma (S.C.); yazmed2013@gmail.com (M.Y.); mohammedelmoudden@gmail.com (M.E.-M.); amahmid1969@gmail.com (O.A.); houda.itouni@uit.ac.ma (H.I.)
*Corresponding author
2. Multidisciplinary Research Laboratory in Didactics, Education, And Training, Department of Life and Earth Sciences, Regional Center for Education and Training Professions, Marrakech-Safi, Main Headquarters, Marrakech, Morocco
3. Laboratory of Natural Resources and Sustainable Development, Ibn Tofail University, Kenitra, Morocco
Email: elmostapha.bouhamid@uit.ac.ma (E.M.B.); chakiri@uit.ac.ma (S.C.); yazmed2013@gmail.com (M.Y.); mohammedelmoudden@gmail.com (M.E.-M.); amahmid1969@gmail.com (O.A.); houda.itouni@uit.ac.ma (H.I.)
*Corresponding author
Manuscript received August 4, 2025; revised August 25, 2025; accepted December 4, 2025; published May 13, 2026
Abstract—The main objective of this study is to assess the extent to which machine learning models can predict the effectiveness of Scientific Inquiry Approaches (SIA) in learning and teaching within Moroccan secondary education. Data from 1,392 students, collected through classroom observations and interviews during the period 2021–2023, were used to develop a predictive analytics framework. The dataset was meticulously preprocessed by applying missing value treatment and normalization techniques to ensure robustness. The K-Nearest Neighbor (KNN) algorithm was implemented using the scikit-learn Python library. Model performance was evaluated using multiple metrics, including accuracy, precision, recall, F1-Score, specificity, false positive rate, Receiver Operating Characteristic (ROC) analysis, and Area Under the Curve (AUC). The results demonstrate strong predictive performance, with an AUC of 0.8875 for interview-based data and 0.9309 for observation-based data, corresponding to prediction accuracies of 90.2% and 94.5%, respectively. These findings indicate that machine learning is an effective tool for predicting the success of SIA teaching approaches. By integrating complementary data sources, this study provides novel evidence from the Moroccan educational context regarding prediction reliability. The findings have important implications for improving instructional practices, supporting data-driven decisionmaking, and informing educational policy. Future research may further validate the proposed framework by exploring additional machine learning algorithms and broader datasets.
Keywords—scientific inquiry approaches machine learning, secondary education, predictive analytics, educational technology
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—scientific inquiry approaches machine learning, secondary education, predictive analytics, educational technology
Cite: El Mostapha Bouhamid, Said Chakiri, Mohamed Yazidi, Mohammed El-moudden, Omar Amahmid, and Houda Itouni, "Predicting the Effectiveness of Scientific Inquiry in Educational Technology in Moroccan Secondary Schools: A KNN-Based Analysis through Observations and Interviews," International Journal of Information and Education Technology, vol. 16, no. 5, pp. 1196-1209, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).