Using Gradient Boosting Algorithms in Predicting Student Academic Performance

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

Predicting students’ academic performance is a crucial initiative in the field of education, as it allows educators and administrators to spot students who may require extra assistance, customize educational resources to meet individual requirements, and improve overall educational results. Conventional approaches to forecasting academic achievement, such as statistical analysis and expert evaluation, have certain drawbacks in terms of precision and scalability. The emergence of machine learning (ML) methods provides a possible alternative by utilizing extensive datasets and advanced algorithms to reveal patterns and generate more precise predictions. This investigation’s primary goal is to explore the predictive modelling of student academic performance by improving the accuracy of predictions through the utilization of machine learning techniques. The study was conducted utilizing the Python programming environment. The prediction of student academic performance was carried out utilising the Bidirectional long short-term memory (Bi-LSTM) based Weighted Cost Effective Random Forest algorithm. The study utilized the Deep Encoder CNN-Bi-LSTM for optimal feature extraction to foresee student academic performance. The extracted features were then classified using the Weighted Cost Effective Random Forest (WECRF) classifier, and the classification was evaluated in terms of accuracy, precision, specificity, sensitivity, and recall. The issues addressed include the class imbalance, computational complexity, cost, and huge dimensional issue, among others. The random forest method achieved Precision Score - 0.72, Recall Score - 0.68, F1 Score - 0.69, and Accuracy - 0.77 in this study. Moreover, the suggested technique facilitates the automated forecasting and enhancement of students' future academic performance. Keywords: Predictive Modelling; Students; Academic Performance; Machine Learning; Bi-LSTM; CNN-Bi-LSTM; Deep Learning.

Introduction: Educational Data Mining and Machine Learning Models gained more interest among researchers and academicians in recent years. These are used to extract meaningful data from the educational database can be applied to predict the student academic performance. Objective: The main objective of this research shows the capability to predict the student's performance and is highly beneficial to take remedial actions in the present educational system. It also offers suitable student assistance, allows an educational institution or teacher to help the students in gaining extra marks. Methods: The presented model operates in two stages, namely classification and outlier detection. Initially, a multilayer perceptron is employed for data classification. Later, a stochastic gradient descent classifier and multi-layer perceptron are integrated to effectively classify the data. For further improvement in the classification, Student academic performance, outlier detection method namely radial basis function is applied to remove the misclassified instances. Results: The proposed model achieves superior classification performance with the maximum precision of 79.30, recall of 79.30, and accuracy of 79.30, F-score of 79.30 and kappa value of 52.40 respectively. The simulation outcome exhibited that the multilayer perceptron and stochastic gradient descentmodel offers better results over the other classifiers. However, the usage of outlier detection using the radial basis function model takes the classifier results to the next level. Conclusion: Using classification techniques a new student academic performance model is proposed by addressing some issues in the existing model which is used to predict the student academic performance assist inproper time.

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.

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

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.

The study investigates the relationship between self-leadership and the academic performance of students. The research was quantitative in nature, utilizing a survey method. The study was carried out in various departments of the Women University of AJK Bagh, with 326 students selected as the sample through simple random sampling. A close-ended questionnaire was adopted to collect data from the selected students. After data collection, statistical technique Pearson correlation was employed for analysis. The findings revealed a strong positive correlation between self-leadership and academic performance of university students. The study's implications suggest integrating self-leadership concepts and activities across different academic disciplines to underscore its significance and provide practical application opportunities for students. By emphasizing self-leadership, educational institutions can potentially enhance students' motivation and performance, ultimately contributing to their overall success.

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.

The purpose of this study was to examine the relationship between social media usage and academic performance of undergraduate students of Sule Lamido University Kafin Hausa. The study was guided by sixteen objectives with three research questions and thirteen hypotheses. The study adopted Social Learning Theory by Albert Bandura (1977) and Student Involvement Theory by Alexander W. Astin (1984). The study adopted correlation and survey research designs. The target population was 5,321 undergraduate students of Sule Lamido University, Damaturu. The sample size was 273 participants. Instrument used to collect data for this study was the Social Media Usage Questionnaire (SMUQ). The Questionnaire items was validated by seven validators. Expert judgment method was used to calculate the validity index and the validity index was found 0.7. Cronbach's Alpha was used to determine the reliability coefficient of the questionnaire and items was found to be reliable at 0.86. The data were analysed using frequencies, percentages, Pearson Product Moment Coefficient (PPMC) and independent samples t-test. Findings showed that: WhatsApp is the most commonly use social media site followed by Facebook, Twitter, Instagram, YouTube and Wikipedia. Level of students' usage to social media types was rarely. Undergraduate students of Sule Lamido University use to spend 0-1 hour in a day on social media sites. Some of the reasons for students' usage of social media sites are; learning purpose, communication and interaction, socialisation and leisure; business, politics and regious activities. There was a significant relationship between the use of Facebook, WhatsApp and academic performance of students. It was also revealed that there was no significant relationship between the use of Twitter, Instagram, YouTube, Wikipedia and academic performance of students. There was a significant gender difference in the use of Facebook, WhatsApp and academic performance of students. There was no significant gender difference in the use of Twitter, Instagram, YouTube, Wikipedia and academic performance of students. The study concludes that, the lecturers, school guidance counselor and parents in particular should collaborate and monitor the behaviour of students towards the use of social media sites. The study recommends the introduction of guidance and counselling specifically meant for social media affairs at the University to help in the mitigation of the social media issues that influence students learning.

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.

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.

Purpose: To study the association between attendance and academic performance of MBBS students belonging to a private medical college in the subject of ophthalmology. Study Design: Cross sectional observational study. Place and Duration of Study: Akhter Saeed Medical and dental college of Lahore, from January 2021 to October 2021. Methods: A total of 152 students were included in study. The attendance record and test records of students in the subject of ophthalmology was retrieved. Both class test and ward tests included Short essay questions and multiple choice questions. Relationship between the attendance and test results was compared by using Pearson Correlation. Results: Among 152 students, 62.5%were females and 37.5% were males. The mean attendance in 3rd year was 55.73 ± 20.44 and in 4th year was 77.25 ± 19.03. During 3rd year MBBS, 80.92% students had attendance below 75% and in 4th year 32.24% had below 75%. Passed students had mean attendance of 56.82% in 3rd year and 78.74% in 4th year. Failed students had mean attendance of 49.27% in 3rd year and 40% in 4th year. Regarding ward test, students who passed their batch test in first attempt had mean attendance of 60.92% and 83.54% in 3rd year and 4th year respectively. Positive relationship between pass candidates in send up exams and ward tests was demonstrated by Pearson Correlation, showing significant results at 0.01. Conclusion: The academic performance of students is directly related with class attendance. The students with better class performance had better percentage of attendance and vice versa. Key Word: Medical students, attendance, academic performance.

Background and Aim: Many factors have an impact on academic performance, cognitive function one of the most important factor which affect the academic performance, cognitive means conscious mental activity as thinking, remembering, learning or using language. The objective of the study was to assess association between the oxygen saturation level, cognitive function and academic performance of medical students. Methods: This descriptive cross-sectional study conducted between October 2016 to April 2017. Simple random sampling of 88 medical students were included in the study. A self-administrated questionnaire including factors affect the oxygen saturation levels, measurement of the oxygen saturation by pulse oximeter, and digital span test which include forward and backward test. The data from this research were analyzed using mean descriptive and inferential statistical tests, standard deviation, and t-test and ANOVA with a significance level of p < 0.05. Results: Results showed that academic performance of the students had the highest correlation with the cognitive functions (p

It has been observed that majority of students do not have effective study habit skills that are proportional to their academic demands. In recent times, reports of large scale academic failures among Nigerian students have become a subject of concern for stakeholders. Although, a lot have been written on study habits and academic performance of students in many areas but the effects of study habits on academic performance of CRS students seems to be a neglected area. This study, therefore, attempts to isolate time allocation and management as a factor of study habits and examine its effects on the academic performance of CRS students in Federal College of Education, Zaria. The following hypothesis that &ldquo;there is no significant difference in the opinions of CRS students on the effects of time allocation and management on academic performance in Federal College of Education, Zaria&rdquo; guided the study. Primary and secondary data used in the study were collected through Study Habit Inventory and other relevant materials.&nbsp; The primary data were analyzed using tables and simple percentages while independent t-test was employed to test the hypothesis.&nbsp; The results revealed that majority of CRS students at NCE level in Federal College of Education, Zaria used poor time allocation and management which was likely to have affected their academic performance. It was therefore recommended, among others, that students should be counseled on the effective time allocation and management.