Development of mobile-interfaced machine learning-based predictive models for improving students' performance in programming courses

Student performance modelling (SPM) is a critical step to assessing and improving students' performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual influences of hidden factors that are peculiar to students, lecturers, learning environment and the family, together with their overall effect on student performance have not been exhaustively investigated. In this paper, Student Performance Models (SPM) for improving students' performance in programming courses were developed using M5P Decision Tree (MDT) and Linear Regression Classifier (LRC). The data used was gathered using a structured questionnaire from 295 students in 200 and 300 levels of study who offered Web programming, C or JAVA at Federal University, Oye-Ekiti, Nigeria between 2012 and 2016. Hidden factors that are significant to students' performance in programming were identified. The relevant data gathered, normalized, coded and prepared as variable and factor datasets, and fed into the MDT algorithm and LRC to develop the predictive models. The developed models were obtained, validated and afterwards implemented in an Android 1.0.1 Studio environment. Extended Markup Language (XML) and Java were used for the design of the Graphical User Interface (GUI) and the logical implementation of the developed models as a mobile calculator, respectively. However, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE) and the Root Relative Squared Error (RRSE) were the metrics used to evaluate the robustness of MDT and LRC models. The evaluation results obtained indicate that the variable-based LRC produced the best model in terms of MAE, RMSE, RAE and the RRSE having yielded the least values in all the evaluations conducted. Further results obtained established the strong significance of attitude of students and lecturers, fearful perception of students, erratic power supply, university facilities, student health and students' attendance to the performance of students in programming courses. The variable-based LRC model presented in this paper could provide baseline information about students' performance thereby offering better decision making towards improving teaching/learning outcomes in programming courses

The Impact of Coating Ingredients on the Aging Resistance of Topcoat Paints by Model Trees

Topcoat paint is mainly composed of resin and pigment and hence its quality highly depends on the type and proportion of these two ingredients. This study aims at testing the formula of the topcoat paint for finding one that can achieve better quality for anti-aging. Various formulas of paint are applied on boards that will be put into ultraviolet accelerated test machines to simulate weathering tests. The gloss and color, before and after the tests, are collected and numerical prediction method M5P is used to grow model trees for discovering the key factors affecting aging. Based on the structure and the linear regression models in the trees, a better topcoat paint should be composed of a high proportion of resin and generally a low proportion of pigment. Good types of resin and pigment are also identified for keeping color and gloss

Teaching and learning analytics applied to programming courses

La enseñanza de la programación requiere del desarrollo de habilidades cognitivas de alto orden, lo que exige un gran esfuerzo por parte de estudiantes y profesores. Las altas tasas de fracaso académico indican que es necesario tomar medidas para revertir esta situación. La analítica de la enseñanza y el aprendizaje proporciona métodos, procesos y técnicas que permiten mejorar la calidad del proceso educativo. La investigación presenta una revisión sistemática de estudios en los que se aplican técnicas, métodos o procesos de análisis de la enseñanza y el aprendizaje en cursos de programación inicial en el contexto de la educación superior. El objetivo principal es identificar las principales perspectivas y tendencias en la analítica de enseñanza y aprendizaje aplicada a la programación y posibles temas de investigaciónTeaching programming requires the development of high-order cognitive skills, which demands a great effort from students and teachers. The high rates of academic failure indicate that it is necessary to take action to reverse this situation. Teaching and Learning Analytics provide methods, processes, and techniques that allow improve the quality of education process. The research presents a systematic review of studies in which techniques, methods or processes of teaching and learning analysis are applied in initial programming courses in the context of higher education. The main purpose is to identify the main perspectives and trends in teaching and learning analytics applied to programming and potential research topic