Potential Future Directions in Optimization of Students’ Performance Prediction System

Ahmad, Sadique; El-Affendi, Mohammed A.; Anwar, M. Shahid; Iqbal, Rizwan

doi:https://doi.org/10.1155/2022/6864955

Computational Intelligence and Neuroscience

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Computational Intelligence for Intelligent Human-Machine Systems

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Review Article | Open Access

Volume 2022 | Article ID 6864955 | https://doi.org/10.1155/2022/6864955

Potential Future Directions in Optimization of Students’ Performance Prediction System

Sadique Ahmad,¹Mohammed A. El-Affendi,¹M. Shahid Anwar,²and Rizwan Iqbal³

Academic Editor: Zhongxu Hu

Received19 Feb 2022

Accepted26 Mar 2022

Published17 May 2022

Abstract

Previous studies widely report the optimization of performance predictions to highlight at-risk students and advance the achievement of excellent students. They also have contributions that overlap different fields of research. On the one hand, they have insightful psychological studies, data mining discoveries, and data analysis findings. On the other hand, they produce a variety of performance prediction approaches to assess students’ performance during cognitive tasks. However, the synchronization between these studies is still a black box that increases prediction systems’ dependency on real-world datasets. It also delays the mathematical modeling of students’ emotional attributes. This review paper performs an insightful analysis and thorough literature-based survey to draw a comprehensive picture of potential challenges and prior contributions. The review consists of 1497 publications from 1990 to 2022 (32 years), which reported various opportunities for future performance prediction researchers. First, it evaluates psychological studies, data analysis results, and data mining findings to provide a general picture of the statistical association among students’ performance and various influential factors. Second, it critically evaluates new students’ performance prediction techniques, modifications in existing techniques, and comprehensive studies based on the comparative analysis. Lastly, future directions and potential pilot projects based on the assumption-based dataset are highlighted to optimize the existing performance prediction systems.

1. Introduction

Over the past few decades, students’ performance has been predicted while evaluating the influence of different factors, such as emotional attributes, family attributes, study schedule, institutional attributes, and students’ scores in assignments, quizzes, and final examinations [1–5]. Such systems provide useful applications to a wide area in academia, i.e., students’ success and failure estimation due to influential factors [6–10]. This study splits the earlier contributions into two groups. The first group consists of insightful psychological studies, data mining discoveries, and data analysis findings that indirectly contribute to the optimization of students’ performance prediction systems. The second group reports the optimization of existing prediction systems based on the findings of the first group. However, the extensive synchronization between the two groups is still a black box that ultimately increases students’ performance prediction systems’ dependency on a real-world dataset. Such synchronization can provide useful ideas during the optimization and data collection process. It also paves the way for an assumption-based dataset to prove the viability of pilot project implementations that will speed up modeling students’ emotional attributes.

This review paper conducts an insightful study and literature-based survey to draw a comprehensive picture of the prior studies on student performance analysis and prediction. The review consists of 1497 articles from 1990 to 2022 (32 years), which reported various information for future researchers:1.It explores and lists the research fields’ contributions focusing on students’ performance optimization. Psychology and data analysis fields pave the way for effective solutions to the problems of data deficiency. They provide qualitative findings that can be used for creating an assumption-based dataset for pilot project implementation.2.It thoroughly considered new and modified algorithms that predict students’ performance. Also, a comparative analysis was performed between the existing students’ performance prediction approaches to provide better recommendations for optimization.3.The study delivers a comprehensive picture of potential challenges and research direction for future researchers. The review also shows that very few contributions have mathematically modeled emotional attributes.

The remaining sections of this review are as follows: Section 2 gives a detailed literature review. Section 3 elaborates the review methodology, and Section 4 produces data evaluation. Section 5 presents future challenges, and Section 6 concludes the study.

2. Literature Review

Students’ performance prediction systems have enormous applications in academia, such as predicting at-risk students, course recommendation, and basic counseling against negative emotions, highlighting the influence of institutional attributes, family factors, etc. [11–14]. It is also needed to advance the academic achievement of excellent students [15–19]. Prior studies deliver qualitative and quantitative results in extending students’ performance evaluation and calculation, and highlighting the factors that influence the performance [20–25]. For a few decades, psychology, data mining, cognitive computing, and data analysis fields directly or indirectly contributed to the optimization of students’ performance prediction systems [26–34]. Therefore, related work is split into the following subsections.

2.1. Contributions of Psychology

Psychological studies results manifest that students’ performance is easily influenced via emotional attributes, such as frustration, anxiety, stress, over expectation of parents, and parents’ relationship [35–37]. The results provide correlations statistics among emotional factors and the expected performance of students in cognitive activity, such as attempting the examination, quizzes, assignments, class activities, and extracurricular activities. These particular emotional factors can negatively and positively impact the students’ performance. In such a situation, emotional severity, family attributes, and institutional factors play a crucial role in influencing performance [38–40]. It shows that performance is always very sensitive and affected by the individuals’ surroundings.

2.2. Contributions of Data Mining

The data mining evaluates the relationship among various students’ factors, such as the role of emotional factors, family attributes, institutional factors, and class performance. Such studies provide good opportunities for accurate estimations of expected students’ performance [41–47]. The meaningful patterns always produce good directions for further exploration. The previous articles lack coordination between the students’ influential attributes and academic performance. The literature lacks accurate techniques to simulate students’ performance due to the insufficient synchronization and coordination among earlier studies on students’ factors. The mathematical modulation of students’ performance needs to formulate the function of student factors. However, it is inspiring to closely examine the quantitative influence of several student factors on academic achievements. The earlier studies show that emotional, family, study schedule, and institutional attributes are the significant factors that can easily influence students’ academic performance in any critical cognitive activity. Prior studies illustrate that educational data mining practices contribute to students’ factor evaluation process and performance prediction. Institutional factors involve teaching methodology, engaging students in the classroom, and the vision of instruction. According to literature studies, teachers play an active role in institutional attributes influencing students’ performance. They provide administrative assistance and assistance in ensuring discipline [48–57].

2.3. Synchronization among Existing Studies

Accurate performance prediction needs to examine students’ factors beyond the computer science framework. The literature studies are still limited in finding an authentic and extendable approach that overlaps psychology, data mining, data analysis, and cognitive research. Articles have various solutions to predict students’ performance using different techniques that could have the potential to be escalated to more general problems of predicting student performance [58–62]. The primary objective of the current review attempt is to efficiently explore the relationship between students’ factors (as mentioned earlier) and their performance. Therefore, the literature is studied with the selected students’ attributes (emotional, family, study schedule, and institutional) and effects. Articles show that most students do not participate in extracurricular activities, believing extra activities would negatively affect their academic achievements. Earlier studies also focus on predicting college students’ performance by considering all the important aspects. They delivered a prediction system to estimate performance by assisting the university in selecting each candidate using past academic records of students granted admissions [29, 63–69]. Such efforts show that earlier studies contribute to decreasing the number of at-risk students and advancing the performance of excellent students.

Literature also attempted to perform a survey on classroom learning in different environments. It analyzes various aspects and factors influencing (positively or negatively) performance in a classroom that interfere with learning. This paper presents a systematic review of numerous studies on students’ performance in classroom learning. For a few decades, the research has produced numerous results in students’ performance evaluation; however, the education system needs a complete and detailed performance prediction system that can ensure interaction and coordination between the aforementioned students’ factors. Literature studies delivered various contributions, such as the proposal of an innovative model that targets modifying learning sustainability through smart education applications and regression and correlation among students’ factors, and logistic regression analyses generated that being female, first-semester GPA, number of courses per regular semester, and number of courses per summer semester were imperative predictors of baccalaureate degree achievement [70–77].

2.4. Existing Models and Performance Prediction System Optimization

Studies have focused on applying artificial neural networks to predict performance in different environments. Articles are also saturated with deep learning techniques that deliver prediction and highlight at-risk students. Few other technologies provide opportunities to accurately evaluate the performance and reduce the failure rates [78–82]. It also helps in counseling students in alarming situations that can positively impact their academic achievements, i.e., COVID-19. Thus, during the literature survey, we have found many students’ prediction systems which are interesting; nevertheless, they are failed to mathematical model emotional attributes and synchronized them with institutional attributes, study schedules, and family attributes [31, 37, 83–98]. The objective of this study is to identify the relationship between extracurricular activities and students’ performances.

The articles deliver many results on the effects of influential students’ factors. This study explores performance prediction beyond the scope of computer science and machine learning.

2.5. Related Performance Prediction Methods

As discussed earlier, many studies solved meaningful challenges in students’ performance prediction area of research for a few decades. The earlier studies have many contributions in the form of neural works, recommendation systems, course recommendations, and students’ performance evaluation systems [87, 99–109]. The prior studies demonstrate comprehensive work on students’ performance prediction systems that use information obtained during the interaction of students with the institutional attributes. To mathematically consider the expected actions of a students’ factors, such information provides proper guidelines. The significant characteristic is the identical structure of the information processing system of students, which can be replicated to construct a learning algorithm (cognitive architecture). Literature studies are flooded with many findings that primarily contribute to prediction algorithms and mathematical models; nevertheless, modeling the relationship between students’ emotions (frustration, stress, etc.) and students’ performance is very little focused [110–115].

Also, the published studies on modeling emotion are not extendable toward a matured prediction system. So, the dire need is to assess the main framework of existing prediction algorithms. Exploring the qualitative results of psychological studies and data analysis discoveries is needed to estimate students’ performance. It will also help in the iterative calculation of emotional influence on performance during critical cognitive activities.

Extraordinary academic performance is only possible with excellent cognitive skills. Such skills are needed to accomplish any task requiring problem-solving approaches, reasoning, and memory management. However, with inadequate cognitive abilities, an individual cannot achieve an excellent score in various cognitive tasks, i.e., assignments, quizzes, and written examinations. They require students to process new information, organize learning, and retrieve that data (from memory) for later use. So, predicting performance while calculating the intense impact of various groups of factors is crucial not only for tutors to ensure effective teaching methodology but also for students’ achievements and effective academic policies. Earlier studies have delivered many approaches that predict students’ performance; nevertheless, they have paved the way for new challenges for effective educational systems. The skills levels of students are changing as they learn and forget. The educational system needs such a system that can manage the students’ dynamic behavior during cognitive activities.

Other studies have described the students’ personality traits and the essential characteristics of personality. Results reveal that performance prediction design can be broken down into subsections that are more realistic in comparison to other techniques. It also paves the way for the development of performance prediction architectures which were easy to understand. The current review illustrates that the performance prediction system needs to coordinate among prediction architecture, psychological experiments, semantical investigations, statistical analysis, and mathematical formulation. This work provided an outstanding opportunity for researchers belonging to performance prediction, bioinformatics, data mining, and data integration.

3. Review Methodology

The review process is started from the initial screening within the scope of the current attempt. As elaborated earlier, this review focused on recent and state-of-the-art contributions to student performance prediction. Figure 1 illustrates the methodology of the review. We have divided the complete review process into the following sections.

3.1. Review Process

This study reviews the earlier studies thoroughly based on the procedures prescribed by Petersen et al. [116] and Keele [117]. The methodology is adopted from Keele while the study mapping method is copied from Petersen et al. The review process is initiated with the modified procedure, which is demonstrated in Figure 1. For better understanding, the review delivers a detailed methodology of the prior work contributing to student performance prediction directly or indirectly. Moreover, the study put a list of research questions to demonstrate the main objectives. These research questions enable us to choose relevant research studies for screening and investigating the main challenges in students’ performance predictions. Every research question has a list of keywords to explore the literature and learn about a particular question. These keywords are used to search publications, including peer-reviewed book chapters, conferences proceeding, and journal articles.

3.2. Research Questions

1.Q1: what are the applications of student performance prediction systems?2.Q2: what are the factors that can optimize student performance prediction?3.Q3: what is the intensity of research findings in the field of student performance prediction systems optimization?4.Q4: are the findings of psychological studies, data mining, and contribution in algorithms synchronized with each other for the viability of the pilot project?5.Q5: how synchronization and coordination of prior psychological, data mining, and algorithmic findings contribute to the effective educational system via student performance prediction algorithm.

3.3. Searching Keywords

The current study adopted a manual review methodology introduced by Keele [117]. The automatic review presented by Petersen et al. has a few disadvantages [116]. (1) The automatic search is not feasible for the current review [118]. (2) The manual searching strategy gives more relevant studies. Table 1 reflects the list of keywords that have produced a variety of articles published by various publishers, i.e., IEEE, Elsevier, Springer, Hindawi, MDPI, ACM, Wiley, and others. It shows many articles, including journals, book chapters, and conference proceedings.

The keywords were searched directly on publishers’ websites and Google Scholar with a default setting. We have evaluated all the articles and collected those that deliver relevant findings for further screening. Furthermore, the main factors, topics, and relevant studies, including journals, conference proceedings, and book chapters, are given below:1.Emotional attributes2.Family factors3.Study Schedule4.Institutional attributes5.Psychology, data mining, and data analysis findings on the factors as mentioned earlier6.Contribution of cognitive computing, deep learning, and machine learning in students’ performance prediction7.Reviews and comparison

3.4. Screening

Screening of studies is performed with the following terms and conditions:1.The team selected the publications of the more relevant journal, conference, and book chapter.2.Second, we have focused on the relevant title with impressive citations in Google Scholar.3.Third, rapid reviews were performed for further evaluation and data extraction. During the rapid review, we have focused on the abstract and introduction to get some idea about the challenges, motivations, and contributions.

These three steps were performed to create a database for further information extraction and data collection.

3.5. Information Collection

Various information was extracted from the selected publications during the information collection process, which are shown in Table 1 to 4 Also, a spreadsheet was used to record the various information for further consideration of the research questions. The recorded data are shown in the tables mentioned above.

4. In-Dept Analysis

4.1. Q1: What Are the Applications of Student Performance Prediction Systems

A performance prediction system is essential to predict at-risk students to devise a solution for successful graduation and goal achievements, such as special treatment and counseling sessions. Such prediction systems are more challenging due to the significant factors affecting students’ performance. Thus, a systematic review of the literature has been performed to highlight potential issues in predicting student performance. The study also shows the contributions of previous articles beyond the scope of artificial intelligence, i.e., data mining, data analysis, and psychology techniques contributing to performance prediction. Also, this study provides an overview of prediction techniques that have been used to estimate performance. It focuses on how the predictive algorithm can be used to identify key attributes in influencing students’ academic achievements. With the help of data mining and machine learning techniques in education, the study could have a more effective methodology in proposing a new prediction algorithm and modifying existing students’ performance prediction systems. The primary application outcomes of students’ performance prediction are given below.

4.1.1. Prediction of At-Risk Student

It is crucial to predict at-risk students and devise an effective learning environment in classrooms and laboratories. Although the literature studies are saturated with tremendous results, it is still challenging as the prediction system cannot synchronize and mathematically model emotional attributes, family issues, study schedules, and institutional attributes to develop a significant prediction system. The current review’s first target is to highlight the possibilities of predicting at-risk students while coordinating between literature studies.

4.1.2. Advances the Students’ Academic Achievements

The performance prediction system is essential for at-risk, average, and excellent students. The influential factors that drive academic achievement are an eternal global challenge associated with students, families, teachers, and educational policymakers. Exploring these factors benefits all those interested in developing a system for students’ performance prediction worldwide. Suppose the prediction system considers a large number of influential factors. In that case, the academic achievement of excellent students can also be advanced, i.e., the prediction system could highlight problems due to various emotional, study schedules, family, and institute-related attributes.

4.1.3. Monitoring Students’ Behavior

Student behavior plays a significant role in improving academic achievements, such as interaction and attitude with the teachers, seriousness, and unseriousness in the classroom. Articles of psychology and data analysis contribute to student behavior evaluation, merits, and demerits of various aspects of behavior. We need a prediction system that efficiently modulates the relationship between behavior and students’ performance to highlight, monitor, and improve students’ interaction and engagement in the classroom. It is also essential for the institution to devise effective controlling policies to counter and control the demerit of various behaviors. Through such a prediction system, teachers can easily guide their students in setting and achieving academic goals. A teacher can also help students understand their behavior and its impact on others. The adverse effects of behavior can be overcome and later on monitored by supervising students. Such a system enhances the overall reputation of the institution. Other benefits include preventing early school drop-ups and building good relationships among students. According to Kennelly and Monrad [119, 120], the behavioral problem plays a key role in indicating students at risk and highlighting the individuals near to being dropped off at the institute. Therefore, employing strategies to monitor and control student behavior is extremely important for an effective educational system in a society.

4.2. Q2: What Are the Factors That Can Optimize Student Performance Prediction?

The literature studies indicate that many factors influence students’ performance in cognitive activities, such as quizzes, assignments, examinations, and homework. It includes family-related factors, emotional factors, gender description, and institution-related factors. A brief description of these factors is given below.

4.2.1. Family-Related Factors

The parental involvement and their particular influence are two-fold. First, the earlier studies claim that the interaction of parents positively influences performance. It enhances the academic achievements of the student in critical environments. Research results highlight that parents’ friendly attitudes positively affect student performance, such as daily engagement in cognitive activities. Positive parent involvement can advance the performance, and that father or mother is the first teacher who plays the role of an enduring educator. Such research findings show that parents’ positive and active role cannot be underestimated. Second, the overexpectation of parents can push children towards frustration [121, 122]. Parent mostly observes remarkable achievement on social media, so they also start demanding good grades from their children. With such pressure, students are easily frustrated, which negatively influences their academic outcome during cognitive activities, such as assignments, quizzes, and mid- and final-term examinations. So, the role of the parents should be supportive and motivational, which would help against unnecessary pressure.

To achieve a student performance prediction system, we need to consider parental involvement and the aforementioned other attributes, such as the cohabitation status of parents, the relationship among their parents, socioeconomic situation, and the number of children. Prediction systems need to quantize all these attributes to evaluate future student performance properly. If we look into literature studies, a minimal contribution can be evidenced toward mathematical modeling of student performance for a better educational system.

4.2.2. Emotional Factors

Emotional attributes play a fundamental role in impacting student performance during cognitive tasks. The current study discusses severity levels of frustration, anxiety, depression, and stress. The impact of frustration is the natural part of learning as well as the engaging session (for references, see the literature review section). Such emotion is always found during comprehensive cognitive activities. Literature saturated with many qualitative findings focused on the statistical association between student performance and frustration. However, the study has not been evidenced a comprehensive approach to solve the challenges produced by frustration during cognitive tasks.

We need to analyze the performance of excellent, average, and at-risk students while mathematically modeling the relationship between institution-related attributes, students’ emotional factors, and family-related attributes. Also, the teacher can help frustrated students’ through collaborative exercises, group activities, and group assignments [123]. It will help students easily share their confusion and problems with group members to overcome their frustrations in a comprehensive learning environment. An individual can learn better in offline mode with face-to-face interaction as compared to online interaction [124]. Additionally, the COVID-19 outbreak has accelerated the influence of negative emotions on students’ performance. COVID-19 has created a more critical situation for students’ learning and adjusted them to the online environment with fewer resources. Thus, we are in dire need to evaluate the academic development of students while statistically associating the aforementioned factors and mathematically modeling the proposed relationship to prepare for the critical situation [125].

4.2.3. Gender Description

In the literature review section, the study has shown that earlier studies statistically associated students’ performance with emotional attributes and gender description. Students perform differently while considering aging and gender [126]. Both emotion and gender need to evaluate differently during cognitive activities. Literature studies are evidenced with many contributions on gender differences. They show that different gender individuals perform differently during cognitive activities, solving assignments, attempting quizzes, and examinations. Earlier studies depict that gender difference is an independent biological factor whose magnitude is sometimes dependent on other factors such as cultures, socioeconomic condition, language, age, etc. Gender differences play a crucial role in influencing mental abilities and cognitive processing in mathematical tasks, physics, research, reading, and writing. These issues create a big gap between male and female individuals, referred to as natural and biological differences.

4.2.4. Institution-Related Factors

Different institutional factors are directly or indirectly involved in influencing students’ performance. These factors include but are not limited to instructor teaching methodology, interaction with a student advisor, extracurricular activities in the institution, student complaint platform, the distance between the institution and students’ residence, transport facility, and the behaviors of the friends. These all factors have merits and demerits for student performance. The literature studies of psychology and data analysis have enormous contributions to student performance analysis; however, insignificant contributions have been reported in the form of algorithms and mathematical models in students’ performance prediction.

4.3. Q3: What Is the Intensity of Research Findings in the field of Student Performance Prediction Systems Optimization?

Literature reported many challenges because the students’ performance prediction overlaps psychology, data analysis, and mathematical and algorithmic contributions. The intensity of publications in the student performance prediction area is reported below.

4.3.1. Intensity of Psychological Findings

As discussed earlier, we can find many psychological research contributions in the field of student performance analysis, which show that emotional attributes always affect students’ performance during cognitive activities. So, to provide an efficient solution for student performance prediction, the study must need to evaluate the psychological findings that directly or indirectly focus on student performance evaluation.

4.3.2. Intensity of Data Analysis Findings

Data analysis contribution provides a quantitative measurement for student performance prediction. Such research findings pave the way for an accurate mathematical model to better contribute to the performance prediction area of research.

4.3.3. Intensity of Students’ Performance Prediction Systems

The literature is also saturated with student performance prediction techniques focusing on students’ performance prediction in critical cognitive tasks; nevertheless, these findings are not synchronized and linked toward a significant student performance model. So, the main objective of this review paper is to provide an effective platform for future researchers in student performance prediction. It will pave the way for an effective system to predict at-risk students and excellent student performances, which ultimately provides us with the opportunity to enhance their skills and performance.

4.4. Q4: Are the Findings of Psychological Studies, Data Mining, and Contribution in Algorithms Are Synchronized with Each Other for the Viability of Pilot Project?

The intensity of publications contributing to student performance prediction is quite good, but these contributions are not synchronized with each other to mathematically model emotional, family, and institution-related attributes. One of the main objectives of the current review is to highlight the lack of coordination and synchronization of the literature from different research fields. This review would allow future readers of deep learning to collaborate with other research fields.

4.5. Q5: How Synchronization and Coordination of Prior Psychological, Data Mining, and Algorithmic Findings Contribute to the Effective Educational System via Student Performance Prediction Algorithm

Psychological literature produces both qualitative and quantitative findings in students’ performance prediction; nevertheless, the data analysis field highlights the association among students’ factors, i.e., emotional, family, and institutional attributes. If these findings are linked with the objective of qualitative data repositories and algorithms, then, we can move toward an efficient student performance prediction system. The psychological work produces accurate students’ emotional data focusing on their performance. On the other hand, the data analysis field makes the meaningful statistical association and correlation information. The data analysis field of research provides a couple of tests to find the correlation between student emotional attributes and their performance, i.e., Pearson correlation and regression. These tests verify the correlation among different factors.

We are in dire need to have the abovementioned psychological and data analysis findings to propose a comprehensive algorithm. Every part of the student performance prediction area of research is interlinked. The psychological result verifies the emotional change during the evaluations of the frustration, severity, anxiety, and stress. Second, the data analysis findings associate the student attributes. Third, the student performance prediction algorithm mathematically model the statistical association among the student influencing factors and their performance outcome.

4.6. Specific Keywords-Wise Publications

This section intensively discusses the specific keyword-wise research output focusing on students’ performance, emotional factors, and prediction algorithms. The list of keywords is illustrated in Table 1. The study collected articles based on these keywords for further technical assessment. The specific domain for the technical evaluation includes but is not limited to new methods, modifications in prior work, data analysis, psychological findings, application analysis, review work, and comparison. The self-explanatory Table 1 illustrated the intensity of publications in the domain above using the list of keywords.

4.7. Yearly Publications

Literature studies deliver thousand of research findings that directly or indirectly contribute to students’ performance analysis and prediction. As illustrated in Figure 2, 37 published articles were evaluated (1990 to 1994). About 110 articles mainly focus on student performance and students’ study-related factors assessment. They have evaluated those factors that affect students’ performance during cognitive activities (1995 to 1999). From 2000 to 2004, the study included 144 articles on students’ performance and emotional attributes. The number of featured articles increases with time. From 2005 to 2009, we have assessed 310 articles that contributed to performance prediction.

Furthermore, we have collected 557 research studies that mainly focused on prediction algorithms. The researchers delivered a considerable amount articles from 2010 to 2014. We have found a slight decrease in analysis and psychological studies production until 2019; however, an increase was observed in algorithmic work from 2015 to 2019. Thus, 321 studies were considered during this segment of time. Eventually, the study reviewed 18 articles on students’ performance prediction algorithms published from 2020 to 2022. We have collected 1497 articles during the review process of the current study.

4.8. Domain-Wise Evaluation

The review paper carries out a perceptive analysis and literature-based survey to draw out an inclusive representation of the famous publishers who publish the most highly cited research papers. It involves overall 1497 publications, which are selected after searching on Google Scholar. These studies were evaluated upon their relevant findings, such as new methods, modified approaches, statistical findings, and psychological results (for more information see Table 2 and Figure 3). The major part of this review was to assess the existing students’ performance prediction approaches. So, the study analytically assessed new students’ performance prediction measures, modifications in state-of-the-art techniques, and comparative analysis. Additionally, Figure 4 and Table 3 represent the detailed domain-wise and factors-wise analysis.

5. Potential Future Challenges

A large number of factors are involved in influencing students’ performance; therefore, the prediction system needs to be optimized to consider the impacts of different human factors categories. Such factors categories include but are not limited to emotional attributes, study schedule, family attributes, and institutional attributes. Each category consists of multiple factors impacting students’ performance, either negatively or positively. In the literature section, the study provides a detailed discussion of these factors.

5.1. Potential Pilot Projects Based on the Assumption-Based Dataset

The comprehensive synchronization between the earlier studies is still a black box, which increases systems’ dependency on a real-world dataset. The importance of a real-world dataset cannot be avoided; however, the data collection process is time-consuming and need a list of human resources. It delays the optimization of existing approaches, such as modeling students’ emotional attributes. The data collection process could have various anomalies if the researcher does not follow the analysis of earlier studies. The earlier studies offer excellent opportunities to understand the effectiveness of emotional attributes for optimization. Therefore, pilot projects perform key roles in optimizing the existing students’ performance prediction systems. They provide useful ideas during the data collection process. They also pave the way for an assumption-based dataset to prove the viability of novel ideas in students’ performance prediction.

6. Additional Points of Earlier Studies

Data analysis findings explore the hidden patterns and statistical correlation between students’ performance and influential factors. Such opportunities introduce new challenges for students’ performance prediction systems, e.g., conditional probabilities, correlation, and inferencing. Also, data mining studies are evidenced with many findings in students’ performance prediction area of research; nevertheless, they have different limitations, e.g., lack of in-depth investigation of students’ performance based on selected study-related factors, limited scalabilities, limited dataset, and inadequate qualitative approach of data analysis and psychological studies.

Finally, the review shows that various prior students’ performance prediction methods have been proposed in the last decade; however, meagre studies have highlighted the basic need for synchronization among the abovementioned field’s contributions. Therefore, this review provides an exclusive picture of the future challenges in students’ performance prediction (see Table 1 to 4 and Figure 2 to 4). On one hand, Table 4 depicts the intensity of various optimization techniques, review works, and new students’ performance prediction methods. On the other hand, Table 5 represents the acronyms of the selected studies. Remarks and recommendations against each research question are given in the self-explanatory Table 6.

7. Conclusions

The proposed review highlights the potential research opportunities to optimize the students’ performance prediction systems while exploring earlier contributions of different research fields, i.e., cognitive computing, data mining, data analysis, and psychology. The previous studies are still limited in synchronization between the existing contributions of various fields, which negatively impacted the mathematical modeling of emotional attributes. It increased the systems’ dependencies on real-world datasets. Thus, to investigate the potential challenges thoroughly, the study is split into three sections.1.The data mining discoveries, psychological findings, and data analysis results are examined.2.The study performs a domain-wise investigation of the existing methods focusing on students’ performance prediction, i.e., the domain includes new students’ performance prediction techniques, modifications in existing techniques, and comparisons analysis.3.Eventually, future direction and potential pilot project viability are highlighted.

Data Availability

The screening data are available from the corresponding author, upon reasonable request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors would like to acknowledge Prince Sultan University and the EIAS : Data Science and Blockchain Laboratory for their valuable support. Also, the authors would like to acknowledge the support of Prince Sultan University for the article processing charges (APCs) of this publication.

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