Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria

School-leavers' Transition to Tertiary Study: a Literature Review.

The theoretical and empirical literature relating to factors and problems in the transition of students from secondary to tertiary level education is reviewed here. Studies on persistence and attrition, and on the analysis and prediction of academic performance of students, generally and in particular discipline areas, are included.Transition to university; student performance.

Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach

Massive Open Online Courses (MOOCs) have been experiencing increasing use and popularity in highly ranked universities in recent years. The opportunity of accessing high quality courseware content within such platforms, while eliminating the burden of educational, financial and geographical obstacles has led to a rapid growth in participant numbers. The increasing number and diversity of participating learners has opened up new horizons to the research community for the investigation of effective learning environments. Learning Analytics has been used to investigate the impact of engagement on student performance. However, extensive literature review indicates that there is little research on the impact of MOOCs, particularly in analyzing the link between behavioral engagement and motivation as predictors of learning outcomes. In this study, we consider a dataset, which originates from online courses provided by Harvard University and Massachusetts Institute of Technology, delivered through the edX platform [1]. Two sets of empirical experiments are conducted using both statistical and machine learning techniques. Statistical methods are used to examine the association between engagement level and performance, including the consideration of learner educational backgrounds. The results indicate a significant gap between success and failure outcome learner groups, where successful learners are found to read and watch course material to a higher degree. Machine learning algorithms are used to automatically detect learners who are lacking in motivation at an early time in the course, thus providing instructors with insight in regards to student withdrawal

Developing Student Model for Intelligent Tutoring System

The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching community to understand the learning style of their students and to cater for the needs of their students. One such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful, constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in planning the training path, supplying feedback information to the pedagogical module of the system. Added to it, the student model is the preliminary component, which stores the information to the specific individual learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural network. Further, neural network and psychometric analysis were used for understanding the student characteristic and determining the student’s classification with respect to their ability. Thus, this study focused on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS by applying the neural network and psychometric analysis. The findings of this research showed that even though the linear regression between real test scores and that of the Final exam scores were marginally weak (37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model a good fit for clustering students in groups according to their common characteristics. This finding is in line with that of the findings discussed in the literature review of this study. Further, the outcome of this research is most likely to generate a new dimension for cluster based student modelling approaches for an online learning environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and neural network for student classification makes this study unique towards the development of a new student model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS system for an online learning environment. (Abstract by Author

Factors that influence course difficulty

Throughout the academic community it is generally accepted that there are degrees of difficulty among college courses. The source of variability in course difficulty could be related to background knowledge of students, variability in instructional methods, and variability in the characteristics of the subject matter;Some courses may be more difficult than other courses because of the content of the courses. These are the courses that present students with abstract ideas and are based on the assumption that students have the ability to think in abstract terms. The vocabulary presented in many of these courses is often foreign to students, and the entire course may be perceived as being difficult because terms are difficult to define and place in one\u27s reality;The purpose of this study was to determine factors that influence course difficulty. Because factors which influence course difficulty were identified and understood, it is anticipated that methodology may now be developed to significantly reduce course difficulty;Very few studies have been conducted on course difficulty or factors that influence course difficulty. Teaching techniques, abstractness of concepts covered in classes, and anxiety are sited in the literature as some of the reasons some courses are perceived as being difficult (Horodezkey, 1983; Sworder, 1986; Tanner, 1986). The findings of this study support the literature. Students indicated that little or no experience in a particular area, abstractness of concepts, and anxiety in a particular area caused them to experience more difficulty in some classes;Subjects where the material was interesting or taught in an interesting manner were perceived as being less difficult. Instructors who related concepts to every day events were said to make the classes easier. The social science, arts and humanities areas were perceived as less difficult, because many of the concepts covered in the classes were familiar to students. Students felt that some of the science and math courses were difficult because they were not familiar with the concepts covered

Investigating the Impact of COVID-19 on the First Year Eats Program

First Year Eats (FYE) is a program at Texas A&M University developed to lessen food insecurity and its impact on college campuses. In its three years of development, students in the program have been provided with various cooking lessons that teach participants how to prepare simple meals in a dorm room and food resources such as ingredients and other grocery store items in a community kitchen. To assess the program's success, we have used student GPA as a measure of academic success and two surveys aimed at measuring mental health wellness. The Perceived Stress Scale (PSS) was used to measure stress levels, and the University Belonging Questionnaire (UBQ) was used to measure university belongingness. Students participating in the FYE learning community are compared with a population of learning community students not in the FYE program (NFYE). Three academic years of data, including the first, second, and current students participating in FYE, were investigated. The results of the combined dataset of first- and second-year groups revealed that underrepresented minorities and first-generation students in FYE had a statistically significantly higher GPA than similar students not in the program during the Spring semester of their first year. This significant difference in GPA was not found during the students' Fall semester, suggesting that the FYE program played a role in improving academic performance. The FYE program continues to play a role in academic success and mental health for participants, despite the COVID-19 pandemic. Current students in the program revealed a statistically significant higher Fall midterm GPA for students within FYE compared to NFYE students. This preliminary analysis also indicated that FYE was a factor in predicting midterm grades in Fall 2021. Regarding early PSS and UBQ survey analysis, FYE students in Fall 2021 had a higher level of belongingness than NFYE students. Although the findings were statistically insignificant, additional investigation into the Spring 2022 semester could reveal more about the program's impact on student mental wellness. Further study regarding the effects of remote learning and the pandemic could be investigated in the future for the improvements of the FYE program

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Una revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

Introduction: This article is a product of the research “Ensemble methods to estimate the academic perfor-mance of higher education students”, developed at the Universidad Distrital Francisco José de Caldas in the year 2021, focusing on the review of research work developed in the last five years related to the prediction of academic performance using ensemble algorithms. Objective: The literature review aims to identify the most used algorithms and the most relevant variables in the prediction of academic performance.Methodology: A systematic review of the literature was carried out in different academic databases (Science Direct, Scopus, SAGE Journals, EBSCO, ResearchGate, Google Scholar), using search equations built with keywords.Results: 54 related articles were found that meet the inclusion criteria of the review. Additionally, benefits were found in the application of ensemble methods in the prediction of academic performance.Conclusion: It was found that the most influential variables in academic performance correspond to the aca-demic factor. The algorithm used that presents the best results is Random Forest; in addition to being the most used. The use of these algorithms is an accurate tool to predict academic performance at any stage of university life, and at the same time provide information to generate strategies to improve dropout and academic retention indicators.Introducción: El presente artículo es producto de la investigación “Métodos de ensamble para estimar el ren-dimiento académico de estudiantes de educación superior”, desarrollado en la Universidad Distrital Francisco José de Caldas en el año 2021 y se centra en la revisión de trabajos de investigación desarrollados en los últimos cinco años relacionados a la predicción del rendimiento académico utilizando algoritmos de ensamble.Objetivo: La revisión de la literatura tiene como objetivo identificar los algoritmos más utilizados y las variables más relevantes en la predicción del rendimiento académico.Metodología: Se realizó una revisión sistemática de la literatura en distintas bases de datos académicas (Science Direct, Scopus, SAGE Journals, EBSCO, ResearchGate, Google Scholar), utilizando ecuaciones de bús-queda construidas con palabras claves.Resultados: Se encontraron 54 artículos relacionados que cumplen con los criterios de inclusión de la revisión. Además, se encontraron beneficios en la aplicación de métodos de ensamble en la predicción del rendimiento académico. Conclusión: Se encontró que las variables más influyentes en el rendimiento académico corresponden al factor académico, el algoritmo utilizado que presenta mejores resultados es Random Forest, además de que fue el más utilizado, y que el uso de estos algoritmos es una herramienta precisa para predecir el rendimiento acadé-mico en cualquier etapa de la vida universitaria, y a su vez brindar la información para generar estrategias que permitan mejorar los indicadores de deserción y retención académica