PREDICTING STUDENTS«¤?? GRADE SCORES USING TRAINING FUNCTIONS OF ARTIFICIAL NEURAL NETWORK

The observed poor quality of graduates of some Nigerian Universities in recent times has been traced to non-availability of adequate mechanism. This mechanism is expected to assist the policy maker project into the future performance of students, in order to discover at the early stage, students who have no tendency of doing well in school. This study focuses on the use of artificial neural network (ANN) model for predicting students«¤?? academic performance in a University System, based on the previous datasets. The domain used in the study consists of sixty (60) students in the Department of Computer and Information Science, Tai Solarin University of Education in Ogun State, who have completed four academic sessions from the university. The codes were written and executed using MATLAB format. The students«¤?? CGPA from first year through their third year were used as the inputs to train the ANN models constructed using nntool and the Final Grades (CGPA) served as a target output. The output predicted by the networks is expressed in-line with the current grading system of the case study. CGPA values simulated by the network are compared with the actual final CGPA to determine the efficacy of each of the three feed-forward neural networks used. Test data evaluations showed that the ANN model is able to predict correctly, the final grade of students with 91.7% accuracy.ª¤

Modelling for understanding AND for prediction/classification - the power of neural networks in research

Two articles, Edelsbrunner and, Schneider (2013), and Nokelainen and Silander (2014) comment on Musso, Kyndt, Cascallar, and Dochy (2013). Several relevant issues are raised and some important clarifications are made in response to both commentaries. Predictive systems based on artificial neural networks continue to be the focus of current research and several advances have improved the model building and the interpretation of the resulting neural network models. What is needed is the courage and open-mindedness to actually explore new paths and rigorously apply new methodologies which can perhaps, sometimes unexpectedly, provide new conceptualisations and tools for theoretical advancement and practical applied research. This is particularly true in the fields of educational science and social sciences, where the complexity of the problems to be solved requires the exploration of proven methods and new methods, the latter usually not among the common arsenal of tools of neither practitioners nor researchers in these fields. This response will enrich the understanding of the predictive systems methodology proposed by the authors and clarify the application of the procedure, as well as give a perspective on its place among other predictive approaches

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

oai:flr.journals.publicknowledgeproject.org:article/13Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students’ outcomes. Some authors have suggested that traditional statistical methods do not always yield accurate predictions and/or classifications (Everson, 1995; Garson, 1998). This paper explores a relatively new methodological approach for the field of learning and education, but which is widely used in other areas, such as computational sciences, engineering and economics. This study uses cognitive and non-cognitive measures of students, together with background information, in order to design predictive models of student performance using artificial neural networks (ANN). These predictions of performance constitute a true predictive classification of academic performance over time, a year in advance of the actual observed measure of academic performance. A total sample of 864 university students of both genders, ages ranging between 18 and 25 was used. Three neural network models were developed. Two of the models (identifying the top 33% and the lowest 33% groups, respectively) were able to reach 100% correct identification of all students in each of the two groups. The third model (identifying low, mid and high performance levels) reached precisions from 87% to 100% for the three groups. Analyses also explored the predicted outcomes at an individual level, and their correlations with the observed results, as a continuous variable for the whole group of students. Results demonstrate the greater accuracy of the ANN compared to traditional methods such as discriminant analyses.  In addition, the ANN provided information on those predictors that best explained the different levels of expected performance. Thus, results have allowed the identification of the specific influence of each pattern of variables on different levels of academic performance, providing a better understanding of the variables with the greatest impact on individual learning processes, and of those factors that best explain these processes for different academic levels

Sistema para la predicción del rendimiento de los alumnos en el e-learning

El aprendizaje electrónico o e-learning es una modalidad de enseñanza que ha crecido de manera exponencial en los últimos años. Esto es debido a las grandes ventajas que ofrece, como puede ser la flexibilidad de acceso desde cualquier localización del mundo y a cualquier hora del día, la posibilidad de llegar a un gran número de personas con un aforo ilimitado, y la reducción de grandes costos a empresas e instituciones de educación. Ante la llegada de la pandemia de COVID-19 se ha producido la mayor interrupción de los sistemas educativos jamás producida en la historia, afectando a millones de estudiantes alrededor de todo el mundo. Frente a este escenario, el e-learning ha sido el foco central produciéndose una migración masiva de la enseñanza al marco virtual, evitando que la educación mundial quede bloqueada y dejando más que demostrada la importancia del e-learning. Sin embargo, este cambio de paradigma no es tan sencillo de llevar a cabo, puesto que se trata de una metodología que pone a los estudiantes en el centro del aprendizaje con implicaciones que van más allá de la traslación de la exposición presencial del docente al marco virtual. La principal diferencia de esta metodología de enseñanza respecto a la tradicional es la no presencialidad del docente y, por lo tanto, la no disponibilidad de una tutorización directa entre el alumno y el profesor. Por ello, hay que aprovechar los avances tecnológicos para crear herramientas que permitan ayudar a reforzar la calidad del e-learning. En este Trabajo Final de Máster, partiendo de los datos monitorizados en un curso e-learning, el objetivo será crear un sistema predictivo que sea capaz de conocer cuál será el rendimiento de los estudiantes en las pruebas de evaluación de tipo test basándose en el progreso de estos. Mediante esta predicción, por un lado, los estudiantes podrán conocer cuán preparados van de cara a la próxima prueba de evaluación antes de enfrentarse a ella y, por otro lado, los docentes podrán identificar en una etapa temprana aquellos casos de estudiantes que no están alcanzando una correcta evolución en el curso, pudiendo ofrecerles una asistencia personalizada y evitando así los efectos de cualquier barrera de aprendizaje. Para lograr este objetivo, en primer lugar, se lleva a cabo un proceso de preprocesamiento detallado de los datos para lograr la estructura de datos final necesaria para entrenar los modelos predictivos. En segundo lugar, con el objetivo de aumentar la cantidad de los datos, se lleva a cabo un proceso de generación de datos sintéticos mediante ecuaciones lineales. Por último, se hace uso de diferentes técnicas y enfoques de métodos de aprendizaje automático para lograr la solución final mediante la cual se ha conseguido lograr un rendimiento de predicción muy alto, lo cual hace que el resultado final sea una herramienta muy fiable y útil de cara a la predicción del rendimiento de los usuarios en el e-learning

Factors of learning and predicting success in programming using artificial neural networks

Academic education is one of the key areas in the process of modernization of a country. The ability to predict success helps teachers identify students who have the potential to attend advanced courses, as well as students who need additional education. In modern society programming skills are becoming increasingly important. Many studies show that programming is one of the critical skills of students' technological literacy. Therefore, there is a need to analyze a large amount of data on the basis of which factors that affect student performance in the field of programming can be predicted. In recent years, the application of artificial intelligence in education has increased significantly worldwide. Artificial neural networks (ANN), as one of its tools, are experiencing numerous successful implementations. In the doctoral dissertation Factors of learning and predicting success in programming using artificial neural networks, the ANN model developed for the purpose of predicting the success of students in acquiring programming knowledge and skills is presented. 180 students of the study program Information Technology from the Faculty of Technical Sciences in Čačak were analyzed. Data on previous education were collected for each student. Students' success in learning programming is measured through achievements on the knowledge test and is classified into three categories: unsuccessful, moderately successful and very successful. A three-layer ANN model based on a backpropagation learning algorithm was used to predict student success. 19 models were created. The model with the best predictive accuracy (90,7%) was used as the final model for implementation. A web application was created for that model, with the help of which the teacher has the possibility of adapting the teaching, and more efficient organization of the same, which leads to successfully mastered material