Application of ADDIE Instructional Model to Machine Learning and Assessment of Learner's Outcomes in Higher Institutions in Delta State

The study explored the application of the ADDIE instructional model to machine learning and the assessment of learner's outcomes in higher institutions in Delta State. The study adopted pretest, posttest control group quasi-experimental design. The design comprised of two groups, experimental and control group. In this study, the researcher applied the ADDIE model to the design and implementation of a machine learning course and assess the outcomes of learners using various assessment methods. The population for the study comprised 3,478 300level undergraduate in Delta State University, Abraka and University of Delta, Agbor. The sample of the study comprised 139 300level undergraduates in Delta State University, Abraka and University of Delta, Agbor. Educational Technology Achievement Test (ETAT) and Machine Learning Questionnaire (MLQ) were used for data collection. The two instruments were duly validated. The reliability of ETAT and MLQ were established using Kuder-Richardson 21 and Cronbach Alpha analysis respectively. A reliability coefficient of 0.71 and 0.78, were obtained for ETAT and MLQ respectively. ETAT was administered as pretest and posttest before and after a six weeks treatment using ADDIE and traditional instructional models to students in experimental and control groups respectively. MLQ was only administered at the end of the treatment. Findings showed that ADDIE model provided a structured framework for designing, developing, and evaluating machine learning courses, leading to improved learning outcomes for students. The assessment of learner's outcomes has also been more effective and efficient with the implementation of the ADDIE model. It was recommended that higher institutions in Delta State should provide training and capacity building for educators on how to effectively implement the ADDIE instructional model in machine learning courses. Educators should collaborate and share best practices in the application of the ADDIE model to machine learning to enhance teaching and learning outcomes.