Using educational data mining techniques to increase the prediction accuracy of student academic performance

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

Effects of co-curricular activities on student's academic performance by machine learning

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

The aim of this study is to undertake an empirical inquiry and comparison of the effectiveness of various classifiers with ensembles classifiers in the prediction of student academic performance. A single classifier algorithm will be compared against the performance and efficiency of ensemble classifiers. Reducing student attrition is a major problem for educational institutions all over the world. The search for solutions to increase student retention and graduation rates continues for educators. This is only possible if at-risk students are identified and intervened with as soon as possible. However, the majority of regularly used prediction models are inefficient and inaccurate as a result of inherent classifier limitations and the inclusion of insignificant inputs in their calculations. The majority of data mining and machine learning researcher focused on developing an algorithm that can extract useful information from massive amounts of data after being processed by a computer. The most difficult problem in predictive modelling is identifying the most effective prediction algorithms that are also accurate enough to be useful. Therefore, a multi-level homogeneous ensemble predictive (MLHoEP) model is designed, which uses the different techniques of data mining like feature selection, ensemble learning techniques like boosting and bagging. Seven distinct machine learning algorithms were used on this model to predict and analyse the academic performance of the students. The performance of the classification algorithms in terms of prediction was evaluated using k-fold cross-validation. The study contributes to the body of knowledge by suggesting the development of homogeneous classifiers that may be used to accurately predict students’ academic success. It also proposes the construction of homogeneous classifiers, which may be deployed for accurate student performance prediction, in order to provide a better explanation for the poor performance prediction. As a result of this research, it has been demonstrated that the technique of applying homogeneous ensemble approaches is incredibly efficient and accurate in terms of predicting student performance and assisting in identifying students, who are in danger of dropping out of school. The study compared the accuracy and efficiency of single classifiers to ensembles of classifiers in terms of performance. It was discovered in the research that a homogeneous model with excellent accuracy and efficiency might be developed for anticipating student performance. These key problems have been successfully addressed by the findings of this research study: Which characteristics of students are the most effective predictors of academic performance? How accurate are approaches such as bagging and boosting ensembles for predicting student academic performance? The approach offered in this study will aid educational administrators and policymakers in designing new policies and curriculum-linked to student retention in higher education. This research can also aid in the identification of students who are at risk of dropping out of school early, providing for timely intervention and support. Prospective research will examine the creation and implementation of an automated prediction system known as the students’ academic performance forecast framework, which will collect data from students via online submission and produce a prediction result for their academic performance.KeywordsEducational data miningEnsemble learningMultilayer perceptronRandom forestNaïve BayesCorrelation attribute evaluationInformation gainGain ratio

This study compares the effectiveness of virtual and traditional field trip strategies on academic performance and motivation of students in conservation concepts of biology in North-West Nigeria. The study developed 2 research objectives one of which is to: compare the effectiveness of virtual and traditional field trip strategies on academic performance of students in conservation concept. The study was guided by 2 research questions and 2 null hypotheses. A Quasi experimental research using test-retest method of pretest and posttest was adopted for the study. A sample of 120 N.C.E II biology students comprising of 81 males and 39 females were randomly selected from the population of 357 students of which 213 are males and 114 are females. The study developed 2 instruments namely: Conservation Concepts Performance Test (CCPT) and Conservation Concepts Motivation Questionnaire (CCMQ) which are validated and found reliable with r=0.65. At the end of the treatment data was collected and analyzed using Means and standard deviations to answer the research question while t-test and Mann- Whitney Statistics were used to answer the null hypotheses. The results obtained showed that: significant differences exist between the performance of students taught using virtual field trip strategy and those exposed to traditional field trip strategy. From the findings recommendations were made one of which is: the Ministry of Education/ colleges should provide a biology Virtual environment to serve each school in the region as technology is seen to help improve the performance and raise the level of motivation in biology concepts, moreover it will help to overcome problems faced by teachers from a lack of real field trips which leads to ignorance of some abstract concepts such as conservation concepts biology.

BackgroundThis study aimed to examine differences in socio-demographics and health behaviour between Belgian first year university students who attended all final course exams and those who did not. Secondly, this study aimed to identify weight and health behaviour related correlates of academic performance in those students who attended all course exams.MethodsAnthropometrics of 101 first year university students were measured at both the beginning of the first (T1) and second (T2) semester of the academic year. An on-line health behaviour questionnaire was filled out at T2. As a measure of academic performance student end-of-year Grade Point Averages (GPA) were obtained from the university’s registration office. Independent samples t-tests and chi 2 -tests were executed to compare students who attended all course exams during the first year of university and students who did not carry through. Uni- and multivariate linear regression analyses were conducted to identify correlates of academic performance in students who attended all course exams during the first year of university.ResultsStudents who did not attend all course exams were predominantly male, showed higher increases in waist circumference during the first semester and consumed more French fries than those who attended all final course exams. Being male, lower secondary school grades, increases in weight, Body Mass Index and waist circumference over the first semester, more gaming on weekdays, being on a diet, eating at the student restaurant more frequently, higher soda and French fries consumption, and higher frequency of alcohol use predicted lower GPA’s in first year university students. When controlled for each other, being on a diet and higher frequency of alcohol use remained significant in the multivariate regression model, with frequency of alcohol use being the strongest correlate of GPA.ConclusionsThis study, conducted in Belgian first year university students, showed that academic performance is associated with a wide range of weight and health related behaviours. Future studies should investigate whether interventions aiming at promoting healthy behaviours among students could also have a positive impact on academic performance.

PurposeCreating suitable learning conditions in university classrooms continues to be a topical issue in higher education. The above means that a continued search for innovative teaching and learning strategies in universities remains a priority issue. The study therefore investigated conditions that facilitate the effective implementation of cooperative learning (CL) as well as how CL influences the academic performance of students in universities in Zimbabwe.Design/methodology/approachThe research adopted a quantitative research approach located in the descriptive research design. A sample of 341 undergraduate students selected using a simple random sampling strategy from a sample frame of 701 undergraduate students participated in the study. The study first used a pre-test and later, a post-test to determine the initial and end of semester (EOS) academic performance levels of students, respectively, on a Calculus 1 course. A self-constructed structured questionnaire was also used for data collection on the views of students towards the use of CL with particular reference to the 5 dimensions of positive interdependence (PDI), promotive interaction (PI), individual and group accountability (IGA), social and interpersonal skills (SS) and group processing (GP). The questionnaire was validated using confirmatory factor analysis. Structural equation modelling was used to test proposed relationships. A total of 341 questionnaires were administered through an email survey. Of the total, 149 completed questionnaires were received from the students, giving a return rate of 43.7%.FindingsThe results showed that positive interdependence, promotive interaction, social and interpersonal skills, individual and group accountability as well as group processing were important antecedents to the effective implementation of CL in universities. The results further showed that CL plays a significant role in improving the academic performance of university students; hence, academic performance was viewed as a significant consequence of CL.Research limitations/implicationsThis is the first study on the application of CL to enhance the academic performance of university students in the context of Zimbabwe. As a result, caution should be exercised when generalising the results as more studies to either confirm or disconfirm these results in the context of Zimbabwe may still be required.Practical implicationsThese results have implications on policy and practice with regards to active teaching strategies in universities. With regards to practice, the results demonstrated that the use of group activities that allow students to share knowledge and support each other while the teacher plays a facilitating role is important for enhancing students' academic performance. With regards to policy, the results showed that universities can enhance the academic performance of students if policies that promote student-centered teaching and learning approaches to ensure that teaching in universities is more student-led than lecturer-led are developed.Originality/valueWhile many studies have been conducted in other contexts, to establish the influence of CL on the academic performance of university students, there is no known study on the influence of CL that has been conducted in the context of Zimbabwean universities. The results of this study therefore are an eye opener on the role of CL in enhancing the academic performance of students in Zimbabwean universities.

Predicting student academic performance has been an important research topic in Educational Data Mining (EDM) which uses machine learning and data mining techniques to explore data from educational settings. However measuring academic performance of students is challenging since students academic performance hinges on diverse factors. The interrelationship between variables and factors for predicting performance participate in complicated nonlinear ways. Traditional data mining and machine learning techniques may not be applied directly to these types of data and problems. In this study we develop a classification model to predict student performance using Deep Learning which automatically learns multiple levels of representation. We pre-train hidden layers of features layerwisely using an unsupervised learning algorithm sparse auto-encoder from unlabeled data, and then use supervised training for finetuning the parameters. We train model on a relatively large real world students dataset, and the experimental results show the effectiveness of the proposed method which can be applied into academic pre-warning mechanism.

Education for centuries has focused on the intellect, leaving aside the emotional factor, which impacts the mental health of students and, in turn, academic performance. The objective is to establish the relationship between emotional intelligence with academic performance in university students. It is multicenter with a quantitative approach; sampling is non-probabilistic. The TMMS-24 meta-knowledge trait scale about emotional states was applied to measure Emotional Intelligence. In the academic performance, the general average of the participants was used. There is a relationship between levels of emotional intelligence with academic performance. The information is useful for proposing intervention strategies during the training of emotional competencies from admission.

Introduction Several studies in the literature have highlighted the impact of cognitive impairment and academic performance (AP) in students with sensorineural hearing loss in specialised schools for the deaf. However, few studies have been conducted on this subject in our setting. Purpose The objective of this study was to determine the influence of hearing impairment (HI) and cognitive level on AP in students attending specialised schools for the deaf in Kongo Central. Methods This was a cross-sectional study conducted among 126 deaf students. The parameters collected included sociodemographic, clinical, and paraclinical data. HI was confirmed using pure tone audiometry (PTA) and classified according to WHO criteria. Cognition was assessed using Raven’s Standard Progressive Matrices (SPM), a non-verbal intelligence test, to determine the intelligence quotient (IQ). Data were analysed using SPSS version 26.0. Results A total of 126 students, including 75 boys, were enrolled. The dominant age group was >18 years (44.4%). Most participants were Jehovah’s Witnesses (38.1%) and lived in low-income households (62.7%). Nearly 85.7% of the students had severe to profound HI, with post-lingual onset in 60.3%, and 57.1% had a low IQ. The mean AP was 50.54 ± 6.56%. There was a weak negative correlation between hearing loss and AP (r = -0.30; p < 0.001). Students with above-average IQ had significantly higher AP scores compared to those with low or average IQ (p = 0.005). A significant negative correlation was also observed between advancing age and IQ (p < 0.001), indicating that IQ tends to decline slightly with age, explaining 10.7% of the variance. Conclusion The lack of schooling for deaf children is a significant issue in our context. Advancing age significantly impairs AP, underscoring the need to enrol these students at the appropriate school age.

Every year students success rate was analysed by the Educational Institutions to develop their Academic standard. To identify the success rate many kinds of techniques are used such as statistics, physical examination and currently ongoing datamining techniques. Data mining Techniques was widely used in many fields, it is also used in the Educational environment known as Educational Data Mining (EDM). Educational data mining generate prototype in solving the research problems in students data and used to locate the unseen patterns in the students detailed dateset. This paper uses the EDM to characterize the distinct factors affecting the students performance by making predictions with efficient algorithms. Educational professionals have to identify the causes for the student failure in academic performance and the students not succeed in completing their education which becomes a social problem these days. The machine learning algorithms help the researchers for evaluation of student’s learning habits, their academic performance and added enhancement if required. This paper would discuss different kinds of algorithms to analyse the economic background of the students which mainly affects the students performance. The dataset was utilized from the UCI Repository of secondary school students performance and analysed using the Weka tool for the datamining process.

Background: Suboptimal academic performance by the majority of students at higher educationallevel has been linked with ineffective teaching methods by teachers/lecturers. The aim of this studywas to assess teaching methods, academic, and clinical performances among student nurses in schoolsof nursing, in North-west Nigeria.Methods: The study adopted a mixed approach of a cross-sectional design. The sample comprises of95 tutors, 31 clinical instructors, 698 students, and the final year students from five selected schools. Anadapted questionnaire was used for data collection. Quantitative data were analyzed using descriptiveand inferential statistic with the aid of IBM SPSS Version 20. The qualitative data were summarizeusing thematic content analysis.Results: Lectures, demonstration, and discussion were the most frequently used methods of instructionin the schools. The average academic performances of students were credit (C). The academicperformance of students in the final examination and Nursing Council Exam was “Pass”. Most ofthe students obtained a C-grade in the clinical examination. The clinical performance rating of thestudents in both the school and the nursing Council was “Pass” grade. There was a Significant positivecorrelation between the academic performance of third year students and their clinical performance(<.05). Most students were satisfied with lecture teaching method because of its ability to ensurecurriculum coverage.Conclusions: Clinical performance of students increased with an increased academic performance.Students preferred lectures and demonstration as methods of instruction. The tutors should trained andmotivated students to use students’ centred Learning methods in the administration of their lessons.

Educational data mining concerns of developing methods to discover hidden patterns from educational data. The quality of data mining techniques depends on the collected data and features. In this paper, we proposed a new student performance model with a new category of features, which called behavioral features. This type of features is related to the learner interactivity with e-learning system. We collect the data from an e-Learning system called Kalboard 360 using Experience API web service (XAPI). After that, we use some data mining techniques such as Artificial Neural Network, Naive Bayesian, and Decision Tree classifiers to evaluate the impact of such features on student's academic performance. The results reveal that there is a strong relationship between learner behaviors and its academic achievement. Results with different classification methods using behavioral features achieved up to 29% improvement in the classification accuracy compared to the same data set when removing such features.

The aim of this research study is to find out cause and effect of clinical depression on academic achievements and performance of students. The Main objective of this chronic mental illness is to find out the striking depth of stress and depression on learning outcomes of students. Factors that impact on academic performance caused by clinical depression were investigated. Literature review indicates that clinical depression negatively impact on academic performance of students in terms of lack of confidence, communication problem, time management and trust issues particularly at university level. Public sector university of Karachi was selected as the population of the study. This research is based on the quantitative method. Questionnaire has been set, as a tool for this research. The sample size included 100 students from twenty departments of eight faculties through simple random sampling. Data analysis has been done by descriptive statistical techniques using percentage method. Closed ended questionnaire tool were designed for the collection of data based on five liker scale. The collected was analyzed and results were at moderate level 33% respondents were strongly agree, 47% agree, 13% neutral while strongly disagree and disagree were7%.The findings of this research shows that clinical depression adversely impact on academic performance of the students at University of Karachi. It affects academic performance as well as it has long term impact on student’s mental performance. The hypothesis considered for this research is accepted.

PurposeThe study aims to propose a predictive model that combines personality and demographic factors to predict student academic performance (SAP). This research study works on understanding, enhancing and applying techniques to enhance the prediction of SAP.Design/methodology/approachThe authors gathered information from 305 university students from Al-Zintan University Libya. The study uses a survey questionnaire to collect data on essential variables. The purpose of the questionnaire is to discover variables that affect students' academic performance. The survey questionnaire has 44 closed questions with Likert scale designs that were distributed to a variety of college students at the start of the first semester of 2022. It includes questions about demographics, personality, employment and institutional aspects. The authors proposed a predictive model to identify the main fundamental components, consisting of one dependent variable (SAP) and five independent constructs. The suggested model is tested using partial least squares (PLS) and structural equation modeling (SEM), which perform better than covariance-based structural equation modeling (CB-SEM). PLS-SEM performs well with smaller sample sizes, even for complicated models.FindingsThe study results show that the proposed model accurately predicted the student's academic performance. The personality trait variables are a key factor that determines the actual student's academic performance. The student's academic performance is significantly impacted by each variable in the personality trait variables as well.Originality/valueThe process of validating research was done empirically through the accuracy and efficiency of model performance. The study differs from previous studies in that it accumulated a wide range of factors from different dimensions, including student demographics and personality trait factors. The authors developed a structural equation model to predict students' academic performance.