Multiclass Prediction Model for Student Grade Prediction Using Machine Learning

This work was supported in part by the Ministry of Higher Education through the Fundamental Research Scheme under Grant FRGS/1/2018/ICT04/UTM/01/1, in part by the Speci~c Research Project (SPEV) at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic, under Grant 2102-2021, in part by the Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, and in part by the Malaysia Research University Network (MRUN) under Grant Vot 4L876.Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive analytics used advanced analytics that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. Mostly know that student grade is one of the key performance indicators that can help educators monitor their academic performance. During the past decade, researchers have proposed many variants of machine learning techniques in education domains. However, there are severe challenges in handling imbalanced datasets for enhancing the performance of predicting student grades. Therefore, this paper presents a comprehensive analysis of machine learning techniques to predict the nal student grades in the rst semester courses by improving the performance of predictive accuracy. Two modules will be highlighted in this paper. First, we compare the accuracy performance of six well-known machine learning techniques namely Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN), Logistic Regression (LR) and Random Forest (RF) using 1282 real student's course grade dataset. Second, we proposed a multiclass prediction model to reduce the over tting and misclassi cation results caused by imbalanced multi-classi cation based on oversampling Synthetic Minority Oversampling Technique (SMOTE) with two features selection methods. The obtained results showthat the proposed model integrates with RF give signi cant improvement with the highest f-measure of 99.5%. This proposed model indicates the comparable and promising results that can enhance the prediction performance model for imbalanced multi-classi cation for student grade prediction.Science and Technology Development Fund (STDF)Ministry of Higher Education & Scientific Research (MHESR) FRGS/1/2018/ICT04/UTM/01/1Specific Research Project (SPEV) at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic 2102-2021Universiti Teknologi Malaysia (UTM) Vot-20H04Malaysia Research University Network (MRUN) 4L87

Student profiling in a dispositional learning analytics application using formative assessment

How learning disposition data can help us translating learning feedback from a learning analytics application into actionable learning interventions, is the main focus of this empirical study. It extends previous work where the focus was on deriving timely prediction models in a data rich context, encompassing trace data from learning management systems, formative assessment data, e-tutorial trace data as well as learning dispositions. In this same educational context, the current study investigates how the application of cluster analysis based on e-tutorial trace data allows student profiling into different at-risk groups, and how these at-risk groups can be characterized with the help of learning disposition data. It is our conjecture that establishing a chain of antecedent-consequence relationships starting from learning disposition, through student activity in e-tutorials and formative assessment performance, to course performance, adds a crucial dimension to current learning analytics studies: that of profiling students with descriptors that easily lend themselves to the design of educational interventions

Blended Learning and Bottlenecks in the California State University System: An Empirical Look at the Importance of Demographic and Performance Analytics

In Fall 2014 over 460,000 students enrolled in the 23-campus California State University system; unfortunately, more than 20,000 qualified applicants were denied admission due to capacity and budgetary constraints. In response to continued overcrowding, the Chancellor\u27s Office and Board of Trustees are investigating bottlenecks, defined as anything limiting students\u27 ability to graduate in a timely manner. Blended learning, a pedagogy combining face-to-face and computer-mediated instruction, presents a potential solution to alleviate overcrowding and bottleneck problems. In an effort to investigate the extent to which student demographics and performance analytics explain student success outcomes in a popular blended learning psychology course, an explanatory sequential design was used to study 18,254 students enrolled in the course between 2006 and 2014. In the initial quantitative part of the design, logistic regression and traditional regression analysis were used to determine the predictors of those who chose to drop the course, those who ultimately passed the course, and then to investigate why some students received higher grades than others. Results revealed that race, gender, age, socioeconomic status, and early course participation were key predictors of success. Some of the most significant findings – which included the fact that Mexican American, African American, and Filipino students were less successful in the course than their White counterparts – were examined in more detail in the qualitative part of the study that followed. Specifically, students who self-identified within these race/ethnicities provided a nuanced look at their own course experiences by completing questionnaires and interviews for the study. Thematic findings revealed socioeconomic status, time management, parents\u27 education, and students\u27 campus community as factors contributing to course performance. This study represents one of few large-scale analyses of a blended learning environment focused upon learner outcomes, and it serves to inform the evaluative work surrounding student success interventions, including the ability to predict and understand student risk characteristics for dropping, failing, or performing poorly within a blended learning environment. Understanding the many reasons students engage in less successful behavior may inform student success strategies and alleviate bottlenecks, especially as the prevalence of blended learning courses increases within the California State University system

The Use of Dining Data to Increase Retention and Academic Success in Residential First-Year Students

Higher education leaders have been conducting research over the last 50 years to pinpoint why students enroll in college and then end up leaving. Research shows that there is not a single factor that influences a student’s decision, but it is a variety of factors. Influential factors include class attendance, a sense of belonging, motivation, academic rigor and performance, finances, and more. A student’s physical wellness and mental state can also impact their academic success and life while in college. First-year students often experience depression, anxiety, and loneliness as they try to successfully transition to college. Most of these influential factors are quantified and measured by institutions in real-time through predictive analytics to identify students at risk of leaving. One data point that has not been thoroughly researched is dining data. This non-experimental, causal-comparison study investigated the relationship between dining data and academic success and retention. Analysis of the data showed that dining data can predict academic success and retention, however, the strongest correlation existed between a significant change in dining habits predicting persistence into the next semester. The findings indicate that dining data should be collected by institutions and integrated into predictive analytics to identify at-risk students. Further research should be conducted to generalize the use of dining data in predictive analytics as well as investigate how dining data can be paired with other data points to further identify students in need of assistance

Exploring Learning Analytics In E-Learning: A Comprehensive Analysis of Student Characteristics and Behavior

This article aims to explore learning analytics in e-learning through a comprehensive analysis of student characteristics and behavior. E-learning has become increasingly significant in education, particularly due to the social situation influenced by the pandemic. The Learning Management System (LMS) has become a crucial tool for educators to track and record student learning in e-learning environments. Learning analytics can aid in understanding the context of students, ensuring that they receive a personalized learning experience aligned with learning objectives. However, educators often face challenges in conducting learning analytics for e-learning students, primarily due to the large number of students to analyze and limited data availability. This study seeks to provide a detailed description of e-learning students within the Open and Distance Education (ODE) system. ODE students exhibit high diversity in demographic profiles, learning behaviors, and competency backgrounds. To support this research, we utilize datasets containing student demographic profiles and learning activity data during e-learning sessions. The datasets are obtained from the academic system and LMS log data of Universitas Terbuka. The article employs Exploratory Data Analysis (EDA) and data science approaches as the foundation for predictive and prescriptive analytics of student learning outcomes. Relevant features are extracted from the dataset to build a robust predictive model. The analysis results present patterns and relationships between student characteristics, learning behaviors, and academic achievements. This research aims to provide valuable insights for the development of more effective and personalized e-learning strategies to enhance student learning outcomes in the context of distance education. Moreover, the analysis findings can serve as a basis for informed academic decision-making to improve the quality of e-learning environments

Analytics and complexity: learning and leading for the future

There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward

Predictive Modelling of Student Academic Performance – the Case of Higher Education in Middle East

One of the main issues in higher education is student retention. Predicting students' performance is an important task for higher education institutions in reducing students' dropout rate and increasing students' success. Educational Data mining is an emerging field that focuses on dealing with data related to educational settings. It includes reading the data, extracting the information and acquiring hidden knowledge. This research used data from one of the Gulf Cooperation Council (GCC) universities, as a case study of Higher Education in the Middle East. The concerned University has an enrolment of about 20,000 students of many different nationalities. The primary goal of this research is to investigate the ability of building predictive models to predict students' academic performance and identify the main factors that influence their performance and grade point average. The development of a generalized model (a model that could be applied on any institution that adopt the same grading system either on the Foundation level (that use binary response variable (Pass/ Fail) or count response variable which is the Grade Average Point for students enrol in the undergraduate academic programs) to identify students in jeopardy of dismissal will help to reduce the dropout rate by early identification of needed academic advising, and ultimately improve students' success. This research showed that data science algorithms could play a significant role in predicting students' Grade Point Average by adopting different regression algorithms. Different algorithms were carried out to investigate the ability of building predictive models to predict students' Grade Point Average after either 2, 4 or 6 terms. These methods are Linear/ Logistic Regression, Regression Trees and Random Forest. These predictive models are used to predict specific students' Grade Point Average based on other values in the dataset. In this type of model, explicit instruction is given about what the model needs to learn. An optimization function (the model) is formed to find the target output based on specific input values. This research opens the door for future comprehensive studies that apply a data science approach to higher-education systems and identifying the main factors that influence student performance

What learning analytics based prediction models tell us about feedback preferences of students

Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners

Unfolding the drivers for academic success: The case of ISCTE-IUL

Predicting the success of academic students is a major topic in the higher education research community. This study presents a data mining approach to predict academic success in a Portuguese University called ISCTE-IUL, unveiling the features that better explain failures. A dataset of 10 curricular years for bachelor’s degrees has been analysed. Features’ selection resulted in a characterising set of 68 features, encompassing socio-demographic, social origin, previous education, special statutes and educational path information. Understanding features’ collection timings, distinct predicting was conducted. Based on entrance date, end of the first and the second curricular semesters, three distinct data models were proposed and tested. An additional model was designed for outlier degrees (i.e., a 4-year Bachelor). Six algorithms were tested for modelling. A support vector machines (SVM) model achieved the best overall performance and was selected to conduct a data-based sensitivity analysis. Relevance and impact review allowed extracting meaningful knowledge. This approach unfolded that previous evaluation performance, study gaps and age-related features play a major role in explaining failures at entrance stage. For subsequent stages, current evaluation performance features unveil their predicting power. Also, it should be noted that most of the features’ groups are represented on each model’s most relevant features, revealing that academic success is a combination of a wide range of distinct factors. These and many other findings, such as, age-related features increasing impact at the end first curricular semester, set a baseline for success improvement recommendations, and for easier data mining adoption by Higher Education institutions. Suggested guidelines include to provide study support groups to risk profiles and to create monitoring frameworks. From a practical standpoint, a data-driven decision-making framework based on these models can be used to promote academic success.O sucesso académico é um dos tópicos mais explorados nos estudos sobre o ensino superior. Este trabalho apresenta uma abordagem de data mining para a previsão do sucesso académico no ISCTE-IUL. Numa abordagem focada no insucesso, são estudados os fatores que explicam estes casos. Neste estudo foram utilizados dados de licenciatura de 10 anos curriculares. Foram analisadas 68 características sociodemográficas, origem social, percurso escolar anterior (ensino secundário), estatutos especiais e percurso académico. Foram adotados diferentes vetores de análise para o primeiro ano curricular (entrada e final dos primeiro e segundo semestres curriculares), dando origem a 3 modelos distintos. Um modelo suplementar foi projetado para cursos especiais. Entre os seis algoritmos de modelação testados, SVM obteve a melhor performance, sendo utilizado para a análise de sensibilidade. O processo de extração de conhecimento indicou que fatores como desempenho anterior, interrupções do percurso educacional e idade, demonstram grande impacto no (in)sucesso num estágio inicial. Nos estágios seguintes, fatores de performance atuais revelam um grande poder de previsão do (in)sucesso. A maior parte dos grupos de características faz-se representar, nas características mais relevantes de cada modelo. Estes e outros resultados, como o aumento do impacto dos fatores relacionadas com a idade no final do segundo semestre curricular, suportam a criação de recomendações institucionais. Por exemplo, criar grupos de apoio ao estudo para perfis de risco e criar ferramentas de monitorização são algumas das diretrizes sugeridas. Em suma, é possível criar uma ferramenta de apoio à decisão, baseada nos modelos apresentados, podendo ser utilizada pelo ISCTE-IUL para promover o sucesso académico