Systematic Review of Data Mining in Education on the Levels and Aspects of Education.

doi:10.21203/rs.3.rs-2868229/v1

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Systematic Review

Systematic Review of Data Mining in Education on the Levels and Aspects of Education.

https://doi.org/10.21203/rs.3.rs-2868229/v1

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The application of Data Mining (DM) in education is helping educational leadership to make informed decisions. This review seeks to identify the pattern of DM research by looking at the levels and aspects of education. As the core of schooling is Basic Education (BE), the research seeks to find out the degree of application of DM at this level to identify the challenges and prepare learners for higher education. The databases of nine (9) top-ranked publishers namely; Elsevier, Springer, Hindawi, Sage, SAI, IEEE, MDPI, Emerald and Wiley are used to identify the most recent and relevant papers in Educational DM (EDM) published from the year 2017 to 2022 specifically applied to a specific level of education. After careful filtering, only ninety-four (94) articles and conference papers were fit for the specification. The investigations revealed that only 7.45% of the published research works in EDM for basic education, 11.70% for pre-tertiary education and an overwhelming 80.85% for the tertiary level. Lower levels of education are marginalized. Also, the available literature on educational DM concentrates on student performance using attributes such as demographic factors, family socio-economic life, school environment, learner behaviour and psychological factors among others neglecting the availability of resources to facilitate quality tuition. The use of pedagogical tools is necessary for learning to improve quality. The research has revealed both a population gag and a knowledge gap.

Artificial Intelligence and Machine Learning

basic education

data mining

tertiary

pre-tertiary

teaching and learning

Interestingly, DM in education has played a tremendous role in the application of technology in education helping educational leaders to make accurate decisions (Inusah et al., 2023b). to enhance more accuracy and consistency in educational data for efficient management, data mining is needed (Inusah et al., 2023a). It is almost the same as learning analytics which is emphasizing learning and the outcome (Lemay et al., 2021). As a novel methodology for solving problems in education, much research conducted in this area are basically on pedagogy which enriches the quality of tuition (Sokkhey et al., n.d.-a). Deep learning as a trending DM technique has gained much attention in tracing patterns and detailed solutions to educational problems (Hernández-Blanco et al., 2019a). In a three-decade analysis of EDM, the application of clustering algorithms and their applications, as well as the usability of EDM, has been reviewed (Ashraf et al., 2020a; Dutt et al., 2017). Enhancing more accuracy of using this methodology is necessary (Ashraf et al., 2020a). Research by (Tsiakmaki et al., 2020), considers determining accuracy using the rightful DM techniques. Gamification as a trend for arousing the interest of learners is a novel technique that is combined with EDM to spice up effective teaching and learning (Daghestani et al., 2020), (Arend et al., 2022) . In (Issah et al., 2023)the attributes determining the performance of learners using DM techniques is reviewed. The purpose of this review is to find out the levels of education given attention in EDM and the aspect of education which is usually considered. The research looks at three educational levels; basic education, secondary education and tertiary education. Basic education which is the lower level of education looks at the first 14 years of education. It is seen as the foundation (core) of education. Secondary education looks at the institutions after basic education which is non-tertiary. Tertiary education denotes the higher levels. The various aspects of education that are usually considered at the various levels consist of teaching and learning activities as well as the resources for learning which yields the performance in education. Attributes of learners and many other aspects of learning are considered. Overwhelming research on EDM stresses the attributes of the learner in determining academic performance. The following research questions guide the review;

Which level is given much attention in EDM?
What is the level of research conducted for the basic level in EDM?
Which aspect of education is given much attention in EDM?

The remaining part of the review is organized into six areas. The first part is the related reviews on EDM which comprises the EDM in education, the levels of education (basic, secondary and tertiary) and the major aspects of education ( academic performance, teaching and learning resources and learner attributes). The second aspect is the methodology used in the literature search criteria which comprises how the search was conducted and the search strategy. The publication selection looks at inclusion and exclusion criteria. The third aspect is the presentation of the results for the literature used. The fourth aspect is the discussion of the research gap identified which looks at both the population gap and the knowledge gap. The fifth aspect is the conclusions drawn and the sixth is the recommendations to stakeholders of education.

In (Ashraf et al., 2020a; Dutt et al., 2017), the use of clustering algorithms and their application in EDM is specified using a trend of three decades. The trends of DM shortly are also included in this work. Deep learning approaches to EDM are reviewed to know the task that has already been taken and the future directions (Hernández-Blanco et al., 2019b). At the higher education level, a comprehensive review of EDM is conducted to enable potential authors to know the effects on students' performance and the possible direction to improve effective teaching and learning (Aldowah et al., 2019). Focusing on only classroom learning, (Khan & Ghosh, 2021) used necessary predictors of time, methods and aims to identify significant performance predictions during a course. In a paper (Alshareef et al., 2020), a recommendation for extended tools to measure and predict students' performance in teaching and learning is seen. Using classification models, two attributes; grades and absence are seen as the most significant in determining the performance of students in a year (Fernandes et al., 2019). Video analytics and DM techniques are used in predicting students' performance at the higher education level to know the level of accuracy of the techniques applied (Hasan et al., 2020). In (X. Du et al., 2020) extensive review of EDM at the various levels of education reveals the rapid application of DM techniques across the levels of education but rare information available on K-12 which is the basic or early level of education. The potential influence of DM in identifying the learning process and progress is identified in higher education (Aldowah et al., 2019).

2.1 DM in Education

Applying DM in education is a great discovery to reveal the problems and get accurate solutions. Vital aspects of education ranging from access to education through quality to sustainable management of resources can best be achieved by the use of DM techniques. Accurate planning and provision of educational resources can be done through careful predictions (Inusah et al., 2021). Current trends in EDM focus on the performance of learners at higher levels. Learner attributes that determine the success of learners are not left out. A significant research work dwells on the performance of learners in the tertiary levels of education. This could be due to the larger volume of educational data readily available for analysis. Several techniques in DM can be applied to this big data to enable identifying patterns and tracing challenges of education.

As formal education is hierarchically structured and chronologically graded, success at the lower levels of education is necessary for academic progress. Basic education which is the core or foundation needs more attention than any other level of education. Early learning to gain basic numeracy and literacy skills is the goal of this level. The higher levels of education which gain much attention in EDM are however seen as the peripherals or the crust of education associated with the rewards of education. The admirable nature of this level and the structured system with independent learners make it easier to trace factors that affect the performance of the learner.

2.3 Levels of Education

2.3.1 Basic Education

The starting point for every educational system targets the younger population of school-going ages. In some countries, the age range is from 4–14 years. Other countries expand basic education from 3 to 17 years of elementary education (Inusah et al., 2021). This age cohort needs numeracy and literacy skills to prepare them for future learning (L. Jiang, 2022). Education at this level is compulsory in many countries across the globe to improve citizenship. The vital nature of this level of education makes it a target for many educational policies and interventions. Larger amounts of funding are devoted to this level in the provision of resources for the learners (The Power of Education: Education for All, Development, Globalisation and UNESCO - Colin Power - Google Books, n.d.). Basic education comprises early-grade learning, primary education and lower secondary education. In American English, it is called K-12. These grades are characterized by higher enrolment but with more challenges in teaching and learning resources which affect the performance of learners. Attributes of a learner that hinder success in learning can best be identified at this early level of education. Despite these opportunities for the application of DM to address challenges, little literature is found. Ranging from the performance of the learner, teaching and learning resources provision and the attributes of the learner and the environment, the literature available is insignificant.

2.3.2 Pre-Tertiary Education

This is the next level of education after basic education. It is the level that leverages the success of basic education for a learner to be prepared for tertiary education (Amjad et al., 2022). Specialization in career paths starts at this level of education (Yağcı, 2022a). Some countries have four (4) years of senior high education whilst others have three years. In some cases, it is called senior secondary because it is joined with the lower secondary schools at the basic level. Learners between the ages of 15 to 18 are often in school at this level. Secondary education as the adolescence stage of education is characterized by a lot of learner attributes and challenges related to discipline. Most learners at this stage are in boarding houses where they are with peers. This makes it difficult for them to be controlled. The level is characterized by riots in most countries. The application of DM in the early detection and prediction of challenges at this level is necessary. Despite this, literature at this level in EDM is insignificant. The performance of learners at this level and the grading are similar to the tertiary level. A lot of the data is almost the same as that of the tertiary level yet the application of DM at this level is low.

2.3.3 Tertiary Education

Two-year diploma programmes, vocational-technical programs, four-year undergraduate programmes, master’s degree programmes, Doctor's degree study post-doctoral and research programmes as well as professional degrees all fall under this level of education (Arcinas et al., 2021). It refers to all forms of post-secondary education (Hassan et al., n.d.). This level of education prepares the learners for jobs. It is the level associated with programmes for the highly skilled workforce who will have life-long access to employment for the growth and development of nations. It benefits the individual and the nation as a whole with high economic returns than the other levels. This level is globally the same and recognized in all nations. The quest for the youth to take executive positions for the leadership of countries makes this level competitive. The zeal for self-employment and independence makes it possible for this level to function without much government intervention. Larger data at this level makes it suitable for DM. Several attributes of the learner influencing performance are readily available to the researcher for predictions. This makes the level the most used in EDM.

2.4 Major Aspects of EDM.

The application of DM in education spans a wider aspect of the field of education. Major aspects are the academic performance of learners, teaching-learning resources and learner attributes such as demographic, behavioural, psychological, family background and environmental factors.

2.4.1 Academic Performance

This refers to the success of learners in the academic environment. The ability of the learner to pass or fail an assessment contributes to the progress of learning to higher levels. It is given much attention in DM due to the contribution it makes to the progress of the learner (Yağcı, 2022a). Academic performance is necessary from the lower level of learning to higher levels of learning. However, this aspect of education plays a significant role at the higher levels due to the assigning of grades and certificates to learners which determines employability (Adejo & Connolly, 2018) and (Bonde & Kirange, 2020).

Several factors determine the academic performance of a learner. Among these factors, previous academic performance is a major factor. This makes it easier for institutions to determine the qualification of prospective students for a programme. Factors such as literacy and numeracy skills, punctuality and regularity, determination and family background all affect the performance of a learner. It is worth noting that effective teaching and learning which determines the rate of academic progress is influenced by pedagogical tools availability and the nature of the learner as well as the learning environment.

2.4.2 Teaching and Learning Resources

Pedagogical tools and technological tools that facilitate teaching and learning are major aspects of education that facilitate academic progress. These tools enhance inclusivity and equity in the learning process (Inusah et al., 2022). For a friendly teaching and learning environment to be properly utilized, these tools are necessary. They also determine the efficiency of both the teacher and the learner in the learning environment (Mushtaq et al., 2019). Adequate resource provision for the enrolled learners helps in inclusivity. Exclusionary issues in education which are both digital and physical by nature are a result of inadequate resources. As resources affect academic performance at all levels, the use of DM to determine the availability and the influence on learning and predict the future of teaching and learning is a necessary call. This will help in accurate planning and of resources to enable achieving much academic progress.

2.4.3 Learner Attributes

This aspect centres on knowing the learner. It helps instructors and the school community in general to know the types of individuals they are dealing with. This enables in use of learner centred approach to learning to deal with individual differences in the learning process. The assistance that may be needed for disabled learners can easily be identified through this. Leaner attributes span across wider factors such as parental background, environmental experience as well as the psychological and emotional feelings the learner has been through (Hung et al., 2020a). The influence of learner attributes on academic progress makes it necessary for consideration in EDM at all levels of education (Xing & Du, 2019). Learning institutions can avoid learner challenges by properly scrutinizing the details of a learner before admission.

3.1 Literature Search Criteria

This paper reviews 94 conference and research papers from well-known publishers of EDM and learning analytics-related research. The databases of Elsevier, Springer, IEEE, SAI, Hindawi, MDPI, Wiley, Emeralds and Sage were searched to retrieve EDM papers from the period 2017 to 2022. The papers retrieved were then filtered based on the application of DM to the three educational levels; Basic, Pre-tertiary and tertiary education. Further, the papers were filtered based on three aspects of education; Student performance, teaching and learning resources and attributes of learners. In all, 16 conference papers and 78 research articles met the criteria.

3.1.1 Conducting the Search

To identify relevant papers for the criteria, a search phrase was necessary. The phrase “educational data mining” was used to locate the related literature. Further filtering by specifying the years of publication was done to limit the retrieved papers to the most relevant works from the year 2017 to 2022. Papers that were applied to the various levels of education were further filtered by manually reading the abstract and the methodology to know what the paper is about. Concentration was given to the aspect of education applied in the research; student performance, teaching and learning resources and learner attributes.

3.1.2 Search Strategy

A boolean search was used to locate all necessary publications before filtering. The search string included OR, NOT, AND to enable specifically getting the relevant literature. In all nine (9) databases of well-known publishers, a thorough search was conducted. The nine publishers and the literature rating are in Table 1.

3.2 Publication Selection

3.2.1 The Inclusion Criteria

The most relevant papers to the research were from only conferences and articles from journal publications in the specified publishers. Specifically, research on how EDM is applied to a level of education and an aspect of education was the inclusion criteria.

3.2.2 Exclusion Criteria

Three significant steps were used to exclude research works that were not most recently published and relevant to the research.

Papers that are not relevant to the objectives of the research.
Papers that are not published from 2017 to 2022.
Papers that do not point to a particular level of education and the aspects of education.

3.2.3 Selecting Primary Sources

The selection process included the string “educational data mining” and the use of keywords; education, data and mining. Papers that did not have the string phrase but included the keywords specified were thoroughly read to know the full-text content and include or exclude. To avoid errors and duplications, the primary reviewer's work was reviewed by a second reviewer to ascertain its accuracy and precision. Random selection of the papers under the various categorizations was used as a criterion by the second reviewer. This methodology is the adoption of Okoli's guide for systematic literature review. Figure 1 is the flowchart.

Revelations from the investigation into the already existing literature produced an interesting trend for publications in EDM. For clarity and consistency in the result presentation, the results and findings of the review are presented in line with the research questions. Table 1 is the source and results of the literature search

Table 1

Data Source and Results for Literature Search
S/No	Databases	Total hits	First filtering	Final filtering
1	Elsevier (science direct)	105	53	13
2	Springer	98	65	15
3	IEEE	120	84	26
4	SAI	51	23	5
5	Hindawi	72	31	10
6	MDPI	20	10	7
7	Wiley	15	8	4
8	Emeralds	21	5	3
9	Sage	34	23	11
	Totals	536	302	94

From Table 1, the number of databases searched for the literature totalled nine (9). These databases are for academic publishers of EDM. The total number of responses gotten from the search is 536. After the first filtering, 302 papers were qualified. When the final filtering was done, only 94 papers were qualified to be used in the review. These papers were those that met the inclusion criteria specified in the methodology. Table 2 is the categorization of the qualified papers into the levels and aspects of education. The basic level which is seen as the core has insignificant publications of 7 which is about 7.45%, the pre-tertiary(secondary) has 11 which is about 11.70% and the tertiary has 76 which is about 80.85%. specifically for the aspects of education, academic performance at the basic level recorded only 1 publication, teaching and learning resource management recorded 5 and learner attributes recorded only 1. For the pre-tertiary level, academic performance was recorded 7, teaching and learning resource management recorded 2 and learner attributes also recorded 2. The tertiary level which received overwhelming recognition had 39 for academic performance, 26 for teaching and learning resource management and 11 for learner attributes.

Table 2

EDM as Applied in Levels and Aspects
Level	Aspect	Published Papers
Basic	Academic Performance	(T. Yang et al., 2022)	1
	Teaching and learning resource management	(Mushtaq et al., 2019), (Inusah et al., 2021), (L. Jiang, 2022), (Tsiakmaki et al., 2020) (Inusah et al., 2022)	5
	Learner attributes	(Francis & Babu, 2019)	1
Pre-tertiary	Academic Performance	(Amjad et al., 2022), (Yağcı, 2022a)(Khan & Ghosh, 2020) (Mahmoud & Zohair, 2019), (Rebai et al., 2020)(Livieris et al., 2018) (Rodrigues et al., 2018)	7
	Teaching and learning resource management	(Arfaee et al., 2022) (Rahman et al., 2022)	2
	Learner attributes	(Shao et al., 2020)(MA-Al-Barrak, 2016)	2
Tertiary	Academic Performance	(Francis & Babu, 2019), (Asif et al., n.d.), (Haryani et al., 2022),(Yağcı, 2022) ,(Dabhade et al., 2021),(Hung et al., 2020b), (Aldowah et al., 2019b), (Yousafzai et al., 2021) (Kurdi et al., 2018) (Trakunphutthirak & Lee, 2021) (Hasan et al., 2020), (Lau et al., 2019) (J. Wu, 2022), (Hassan et al., n.d.), (Feng et al., 2022) (Mahmoud & Zohair, 2019) (Sokkhey et al., n.d.-b) (Tsiakmaki et al., 2020) (Fernandes et al., 2019) (Hasan et al., 2020) (Yağcı, 2022c) (Tasnim et al., 2019) (Aldowah et al., 2019) (Ali & Hanna, 2022) (Dutt et al., 2017; Karthikeyan et al., 2020) (Sultana et al., 2017) (Adejo & Connolly, 2018) (Adekitan et al., 2019)(Asif et al., 2017) (Ashraf et al., 2020b) (Chaturvedi, 2017) (Okewu et al., 2021) (Aman et al., 2019) (F Al-Obeidat, 2017)(Mengash, 2020) (Yakubu & Abubakar, 2021) (Widyaningsih et al., 2019) (Adekitan & Noma-Osaghae, 2019)	39
	Teaching and learning resource management	(Hung et al., 2020b), (Arfaee et al., 2022) (Chu et al., 2022),(X. Wu, 2022),(Li et al., 2022), (Zhao, 2022),(F. Wu, 2022),(Peng & Tang, 2022),(Shafiq et al., 2022),(Zhou & Wu, 2022) (Prada et al., 2020) (Daoudi et al., 2021)(al Mazidi & Abusham, 2018)(H. Du et al., 2022)(Chen, 2019) (Tang et al., 2018) (EM Balam, 2010) (Contreras-Luján et al., 2022)(Zhang & Qin, 2018)(Lv, 2021)(J. Jiang & Zeng, 2019)(Hu & Guo, 2018)(Shayakhmetova et al., 2021)(L. Yang, 2021)(Bowen, 2021)(Paredes et al., 2021)	26
	Learner attributes	(do Nascimento et al., 2018) (Leitner et al., 2019) (Martínez-Abad et al., 2020) (Xing & Du, 2019) (Cardona et al., 2020) (Hung et al., 2020a) (J. Wang et al., 2020) (Davies et al., 2021) (Halde et al., 2017) (Xilin & Honglian, 2021) (G. X. Wang, 2018)	11

Conscious efforts are made by researchers such as (Inusah et al., 2021) in tracing problems of education in the field of EDM. However, the relegation of basic education raises a concern as presented in Fig. 2. This supports the (L. Jiang, 2022) assertion that researchers do not have enough information to predict resource management at the basic level. With (T. Yang et al., 2022) alone spotted as the literature for performance and (Francis & Babu, 2019) as the only literature on learner attributes at the basic level, Fig. 3 confirms the insufficient publications though the publications are limited to full manuscript journal research and conference papers as indicated in Fig. 4. It is enough evidence to believe that the basic level is relegated in EDM. Resource management and utilization are essential for effective teaching and learning (Inusah et al., 2022). The basic (foundation) of the success of education in every nation has an insignificant number of research papers in EDM. Challenges of education can be identified at this level of education for lifelong learning of the citizenry. Accurate planning and prediction of the needs of learners in the future can be done to improve teaching and learning. If the foundation is weak, then the entire structure of education will be weaker. Learner attributes must be carefully identified at this stage and used to improve learning for good education.

Proper storage of information in schools at the higher levels of education in databases makes it relatively easier to analyse data. Pre-tertiary level of education has meaningful data available from learners and this information can be used to predict the likely occurrence of events in the schools using the attributes of the learner (Shao et al., 2020). As spotted by(Amjad et al., 2022), (Yağcı, 2022a) and(Khan & Ghosh, 2020) among others, the academic performance of learners at this level of education can easily be predicated using other factors. With only two spotted literature on resource management;(Arfaee et al., 2022) and(Rahman et al., 2022) it is clear that EDM for effective management of resources is insufficiently utilized. Unfortunately, this level of education is not given much attention in EDM. As the progress of learners to tertiary institutions is best determined at this level, the use of EDM to identify the appropriate programme a learner can pursue at the tertiary level should be done with ease. Challenges of education at this level can help both the basic and tertiary levels. Learners’ performance at this level can help stakeholders in basic education to know how to prepare learners whiles the universities will also know how to deal with these learners.

Tertiary institutions are well organized and properly structured to make predictions easier (Francis & Babu, 2019) and (Yağcı, 2022) which makes them suitable for EDM. This could be the reason for the overwhelming research in EDM at this level. A larger chunk of research on EDM is on tertiary institutions where independent learners and their attributes are used in predicting academic performance. In all aspects of EDM, the tertiary level has a majority of publications. This could also be attributed to the universal nature of the tertiary system. However, the research works at this level failed to look at the previous performance and attributes of learners at the secondary level and its effects on the learner at the tertiary level. Also, revisiting learners who complete institutions and return for further studies should be identified. This will help universities to know the true needs of the learner and the demands of society.

As resources are very necessary for effective delivery, conscious efforts by researchers in predicting the effects of resource availability on the performance of the learner are necessary (Inusah et al., 2022). This will help institutions to carefully plan and provide educational resources that can boast the performance of learners. The attributes of learners which also affects the performance of learners should be looked at in the direction of programme choice which affects the occupations for employment. This will enable us in knowing the attributes associated with a particular occupation. In totality, the overwhelming research on EDM centred on the academic performance of learners which in some cases are replication and repetition of research works should be given a different direction. The factors determining the performance of a learner should be given a chance in EDM.

5.1 Population Gap

The population gap identified in this research is the learners at the basic level. These groups of learners are neglected in EDM both in attributes and academic performance. They are not also considered in the provision and utilization of teaching and learning resources for effective learning. This creates a bigger challenge in education as they are not adequately prepared for the pre-tertiary and tertiary levels.

5.2 Knowledge Gap

The knowledge gap in this research is the limitation in the application of data mining in education. Concentration is on the academic performance of learners with little or no attention to the learner attributes and teaching and learning resource management. also, the techniques used are limited to academic achievements will little or no information on the possibility of dropout or the cost of educating the learner.

This paper presents a thorough review of EDM by considering conference papers and articles from nine (9) well-known publishers. All levels of education and research work are considered to know the application of DM and the aspect of the application. The pattern of EDM is identified and the research gaps that need more attention in the area of EDM are identified. In summary, the tertiary level of education is given much attention in EDM at the expense of basic education for growth and development in education. The academic performance of learners is the most recognized aspect of education by researchers with little recognition of resources for a learner. Careful application of EDM at the lower levels of education can help in identifying the challenges of education to gain sustainable solutions for the improvement of the education sector and the development of nations as a whole.

Stakeholders in education must recognize basic education as the pivot for education and assist in the utilization of EDM methodologies to accurately identify the challenges and the appropriate interventions at this level. This is necessary to help in enhancing efficiency in educational management for effective teaching and learning using the available limited resources. It will also help in leveraging the success of education to other levels of education. Adequate provision of resources at this level will help in laying a foundation for the future of education. It will enhance inclusivity and equity to enable every child of school-going age to gain quality education. The progression of children of school-going ages will also be smooth as they have a good foundation of learning.

Competing Interest: The authors declare no competing Interest.

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Adejo, O. W., & Connolly, T. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61–75. https://doi.org/10.1108/JARHE-09-2017-0113
Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24(2), 1527–1543. https://doi.org/10.1007/s10639-018-9839-7
Adekitan, A. I., Salau, O., Ng, A. I., & Adekitan,). (2019). The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, e01250. https://doi.org/10.1016/j.heliyon.2019
al Mazidi, A., & Abusham, E. (2018). Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches. International Journal of Engineering Business Management, 10. https://doi.org/10.1177/1847979018807020/ASSET/IMAGES/LARGE/10.1177_1847979018807020-FIG1.JPEG
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. In Telematics and Informatics (Vol. 37, pp. 13–49). Elsevier Ltd. https://doi.org/10.1016/j.tele.2019.01.007
Ali, A. D., & Hanna, W. K. (2022). Predicting Students’ Achievement in a Hybrid Environment Through Self-Regulated Learning, Log Data, and Course Engagement: A Data Mining Approach. Journal of Educational Computing Research, 60(4), 960–985. https://doi.org/10.1177/07356331211056178
Alshareef, F., Alhakami, H., Alsubait, T., & Baz, A. (2020). Educational data mining applications and techniques. International Journal of Advanced Computer Science and Applications, 11(4), 729–734. https://doi.org/10.14569/IJACSA.2020.0110494
Aman, F., Rauf, A., Ali, R., Iqbal, F., & Khattak, A. M. (2019). A Predictive Model for Predicting Students Academic Performance. 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019, January 2020. https://doi.org/10.1109/IISA.2019.8900760
Amjad, S., Younas, M., Anwar, M., Shaheen, Q., Shiraz, M., & Gani, A. (2022). Data Mining Techniques to Analyze the Impact of Social Media on Academic Performance of High School Students. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/9299115
Arcinas, M. M., Sajja, G. S., Asif, S., Gour, S., Okoronkwo, E., & Naved, M. (2021). Role of Data Mining in Education for Improving Students Performance for Social Change. Turkish Journal of Physiotherapy and Rehabilitation, 32(3), 6519–6526.
Arend, D., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2022). MLPro 1.0 - Standardized reinforcement learning and game theory in Python. Machine Learning with Applications, 9, 100341. https://doi.org/10.1016/j.mlwa.2022.100341
Arfaee, M., Bahari, A., & Khalilzadeh, M. (2022). A novel prediction model for educational planning of human resources with data mining approach: a national tax administration case study. Education and Information Technologies, 27(2), 2209–2239. https://doi.org/10.1007/S10639-021-10699-6/TABLES/14
Ashraf, M., Zaman, M., & Ahmed, M. (2020a). An Intelligent Prediction System for Educational Data Mining Based on Ensemble and Filtering approaches. Procedia Computer Science, 167(2019), 1471–1483. https://doi.org/10.1016/j.procs.2020.03.358
Ashraf, M., Zaman, M., & Ahmed, M. (2020b). An Intelligent Prediction System for Educational Data Mining Based on Ensemble and Filtering approaches. Procedia Computer Science, 167, 1471–1483. https://doi.org/10.1016/j.procs.2020.03.358
Asif, R., Merceron, A., Abbas Ali, S., & Ghani Haider, N. (n.d.). Accepted Manuscript Analyzing undergraduate students’ performance using educational data mining. Computers & Education. https://doi.org/10.1016/j.compedu.2017.05.007
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers and Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007
Bonde, S. N., & Kirange, D. K. (2020). Educational Data Mining Survey for Predicting Student’s Academic Performance. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 31, pp. 293–302). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-24643-3_35
Bowen, D. (2021). Construction of Business English Subject System Based on Data Mining Algorithm. 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education, ICISCAE 2021, 441–445. https://doi.org/10.1109/ICISCAE52414.2021.9590650
Chen, L.-L. (2019). Enhancing Teaching With Effective Data Mining Protocols. Https://Doi.Org/10.1177/0047239519835846, 47(4), 500–512. https://doi.org/10.1177/0047239519835846
Chu, X., Cao, F., Jiao, L., Wang, J., & Jiao, Y. (2022). Optimal Allocation of Higher Education Resources Based on Data Mining and Cloud Computing. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/7067676
Contreras-Luján, E. E., García-Guerrero, E. E., López-Bonilla, O. R., Tlelo-Cuautle, E., López-Mancilla, D., & Inzunza-González, E. (2022). Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis. Mathematical and Computational Applications, 27(2), 24. https://doi.org/10.3390/mca27020024
Dabhade, P., Agarwal, R., Alameen, K. P., Fathima, A. T., Sridharan, R., & Gopakumar, G. (2021). Educational data mining for predicting students’ academic performance using machine learning algorithms. Materials Today: Proceedings, 47, 5260–5267. https://doi.org/10.1016/j.matpr.2021.05.646
Daghestani, L. F., Ibrahim, L. F., Al-Towirgi, R. S., & Salman, H. A. (2020). Adapting gamified learning systems using educational data mining techniques. Computer Applications in Engineering Education, 28(3), 568–589. https://doi.org/10.1002/cae.22227
Daoudi, I., Chebil, R., Tranvouez, E., Lejouad Chaari, W., & Espinasse, B. (2021). Improving Learners’ Assessment and Evaluation in Crisis Management Serious Games: An Emotion-based Educational Data Mining Approach. Entertainment Computing, 38, 100428. https://doi.org/10.1016/J.ENTCOM.2021.100428
do Nascimento, R. L. S., das Neves Junior, R. B., de Almeida Neto, M. A., & de Araújo Fagundes, R. A. (2018). Educational data mining: An application of regressors in predicting school dropout. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10935 LNAI, 246–257. https://doi.org/10.1007/978-3-319-96133-0_19
Du, H., Xing, W., & Zhu, G. (2022). Mining Teacher Informal Online Learning Networks: Insights From Massive Educational Chat Tweets. Https://Doi.Org/10.1177/07356331221103764. https://doi.org/10.1177/07356331221103764
Du, X., Yang, J., Hung, J. L., & Shelton, B. (2020). Educational data mining: a systematic review of research and emerging trends. In Information Discovery and Delivery (Vol. 48, Issue 4, pp. 225–236). Emerald Group Holdings Ltd. https://doi.org/10.1108/IDD-09-2019-0070
Dutt, A., Ismail, M. A., & Herawan, T. (2017). A Systematic Review on Educational Data Mining. In IEEE Access (Vol. 5, pp. 15991–16005). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2017.2654247
F Al-Obeidat, A. T. A. D. B. S. (2017). Analyzing students’ performance using multi-criteria classification. Cluster Computing, 21(1), 623–632.
Feng, G., Fan, M., & Chen, Y. (2022). Analysis and Prediction of Students’ Academic Performance Based on Educational Data Mining. IEEE Access, 10, 19558–19571. https://doi.org/10.1109/ACCESS.2022.3151652
Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. van. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335–343. https://doi.org/10.1016/j.jbusres.2018.02.012
Francis, B. K., & Babu, S. S. (2019). Predicting Academic Performance of Students Using a Hybrid Data Mining Approach. Journal of Medical Systems, 43(6). https://doi.org/10.1007/s10916-019-1295-4
Haryani, C. A., Daicy, W., Widjaja, A. E., Aribowo, A., Prasetya, K., & Hery. (2022). Educational Data Mining: The Application in The University’s Feedback Survey Analysis using Classification and Clustering Techniques. 2022 International Conference on Science and Technology, ICOSTECH 2022. https://doi.org/10.1109/ICOSTECH54296.2022.9829148
Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences (Switzerland), 10(11). https://doi.org/10.3390/app10113894
Hassan, K. M., Khafagy, M. H., & Thabet, F. M. (n.d.). Mining Educational Data to Analyze the Student’s Performance in TOEFL iBT Reading, Listening and Writing Scores. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 13, Issue 7).
Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019a). A Systematic Review of Deep Learning Approaches to Educational Data Mining. In Complexity (Vol. 2019). Hindawi Limited. https://doi.org/10.1155/2019/1306039
Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019b). A Systematic Review of Deep Learning Approaches to Educational Data Mining. In Complexity (Vol. 2019). Hindawi Limited. https://doi.org/10.1155/2019/1306039
Hu, D., & Guo, Z. (2018). Exploring deep integration of information technology and China’s higher education in the era of big data. Proceedings – 6th International Conference of Educational Innovation Through Technology, EITT 2017, 2018-March, 262–267. https://doi.org/10.1109/EITT.2017.71
Hung, H. C., Liu, I. F., Liang, C. T., & Su, Y. S. (2020a). Applying educational data mining to explore students’ learning patterns in the flipped learning approach for coding education. Symmetry, 12(2). https://doi.org/10.3390/sym12020213
Hung, H. C., Liu, I. F., Liang, C. T., & Su, Y. S. (2020b). Applying educational data mining to explore students’ learning patterns in the flipped learning approach for coding education. Symmetry, 12(2). https://doi.org/10.3390/sym12020213
Hussain, S., & Khan, M. Q. (2021). Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning. Annals of Data Science, Ml. https://doi.org/10.1007/s40745-021-00341-0
Inusah, F., Missah, Y. M., Najim, U., & Twum, F. (2022). Data Mining and Visualisation of Basic Educational Resources for Quality Education. International Journal of Engineering Trends and Technology, 70(12), 296–307. https://doi.org/10.14445/22315381/IJETT-V70I12P228
Inusah, F., Missah, Y. M., Najim, U., & Twum, F. (2023a). Agile neural expert system for managing basic education. Intelligent Systems with Applications, 17, 200178. https://doi.org/10.1016/j.iswa.2023.200178
Inusah, F., Missah, Y. M., Najim, U., & Twum, F. (2023b). Integrating expert system in managing basic education: A survey in Ghana. International Journal of Information Management Data Insights, 3(1), 100166. https://doi.org/10.1016/j.jjimei.2023.100166
Inusah, F., Missah, Y. M., Ussiph, N., & Twum, F. (2021). Expert System in Enhancing Efficiency in Basic Educational Management using Data Mining Techniques. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 12, Issue 11).
Issah, I., Appiah, O., Appiahene, P., & Inusah, F. (2023). A systematic review of the literature on machine learning application of determining the attributes influencing academic performance. In Decision Analytics Journal (Vol. 7). Elsevier Inc. https://doi.org/10.1016/j.dajour.2023.100204
Jiang, J., & Zeng, L. (2019). Research on individualized teaching based on big data mining. 14th International Conference on Computer Science and Education, ICCSE 2019, 56–59. https://doi.org/10.1109/ICCSE.2019.8845054
Jiang, L. (2022). Development and Implementation Path of Kindergarten Stem Educational Activities Based on Data Mining. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/2700674
Khan, A., & Ghosh, S. K. (2020). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies 2020 26:1, 26(1), 205–240. https://doi.org/10.1007/S10639-020-10230-3
Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1), 205–240. https://doi.org/10.1007/s10639-020-10230-3
Lau, E. T., Sun, L., & Yang, Q. (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9), 1–10. https://doi.org/10.1007/s42452-019-0884-7
Lemay, D. J., Baek, C., & Doleck, T. (2021). Comparison of learning analytics and educational data mining: A topic modeling approach. Computers and Education: Artificial Intelligence, 2, 100016. https://doi.org/10.1016/J.CAEAI.2021.100016
Li, K., Huang, J., & Lin, J. (2022). The Architecture of College Psychological Teaching Management System Based on Data Mining Technology. Security and Communication Networks, 2022, 1–12. https://doi.org/10.1155/2022/7968756
Livieris, I. E., Drakopoulou, K., Tampakas, V. T., Mikropoulos, T. A., & Pintelas, P. (2018). Predicting Secondary School Students’ Performance Utilizing a Semi-supervised Learning Approach. Https://Doi.Org/10.1177/0735633117752614, 57(2), 448–470. https://doi.org/10.1177/0735633117752614
Lv, X. (2021). A Quality Evaluation Scheme for Curriculum in Ideological and Political Education Based on Data Mining. Proceedings – 2021 13th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2021, 649–652. https://doi.org/10.1109/ICMTMA52658.2021.00149
MA Al-Barrak, M. A.-R. (2016). Predicting students final GPA using decision trees: a case study. International Journal of Information and Education Technology, 6(7), 528–533.
Mahmoud, L., & Zohair, A. (2019). Prediction of Student ’ s performance by modelling small dataset size.
Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470. https://doi.org/10.1109/ACCESS.2020.2981905
Mushtaq, H., Siddique, I., Malik, B. H., Ahmed, M., Butt, U. M., Tahir Ghafoor, R. M., Zubair, H., & Farooq, U. (2019). Educational data classification framework for community pedagogical content management using data mining. International Journal of Advanced Computer Science and Applications, 10(1), 329–338. https://doi.org/10.14569/IJACSA.2019.0100144
Okewu, E., Adewole, P., Misra, S., Maskeliunas, R., & Damasevicius, R. (2021). Artificial Neural Networks for Educational Data Mining in Higher Education: A Systematic Literature Review. Applied Artificial Intelligence, 35(13), 983–1021. https://doi.org/10.1080/08839514.2021.1922847
Paredes, Y. V., Siegle, R. F., Hsiao, I.-H., & Craig, S. D. (2021). Educational Data Mining and Learning Analytics for Improving Online Learning Environments. Https://Doi.Org/10.1177/1071181320641113, 64(1), 500–504. https://doi.org/10.1177/1071181320641113
Peng, W., & Tang, Z. (2022). Construction and Application of Farmers’ Practical Teaching System in Vocational Education Based on Big Data Mining Technology. Computational Intelligence and Neuroscience, 2022, 1–9. https://doi.org/10.1155/2022/6075719
Pu, Y., & Chen, H. (2022). Exercise Recommendation Model Based on Cognitive Level and Educational Big Data Mining. Journal of Function Spaces, 2022. https://doi.org/10.1155/2022/3845419
Rahman, M. M., Watanobe, Y., Matsumoto, T., Kiran, R. U., & Nakamura, K. (2022). Educational Data Mining to Support Programming Learning Using Problem-Solving Data. IEEE Access, 10, 26186–26202. https://doi.org/10.1109/ACCESS.2022.3157288
Rebai, S., ben Yahia, F., & Essid, H. (2020). A graphically based machine learning approach to predict secondary schools performance in Tunisia. Socio-Economic Planning Sciences, 70. https://doi.org/10.1016/j.seps.2019.06.009
Shafiq, D. A., Marjani, M., Habeeb, R. A. A., & Asirvatham, D. (2022). Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review. In IEEE Access (Vol. 10, pp. 72480–72503). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3188767
Shao, Z., Sun, H., Wang, X., & Sun, Z. (2020). An Optimized Mining Algorithm for Analyzing Students’ Learning Degree Based on Dynamic Data. IEEE Access, 8, 113543–113556. https://doi.org/10.1109/ACCESS.2020.3001749
Shayakhmetova, B. K., Ayazhanov, S. S., Omarova, S. E., Ten, T. L., Drozd, V. G., & Medeubaeva, A. M. (2021). Descriptive Big Data Analytics in the Field of Education. SIST 2021–2021 IEEE International Conference on Smart Information Systems and Technologies. https://doi.org/10.1109/SIST50301.2021.9465958
Sokkhey, P., Penh, P., & Okazaki, C. T. (n.d.-a). Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 11, Issue 7).
Sokkhey, P., Penh, P., & Okazaki, C. T. (n.d.-b). Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 11, Issue 7).
Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2020). Implementing autoML in educational data mining for prediction tasks. Applied Sciences (Switzerland), 10(1). https://doi.org/10.3390/app10010090
Widyaningsih, Y., Fitriani, N., & Sarwinda, D. (2019). A Semi-Supervised Learning Approach for Predicting Student ’ s Performance: First-Year. 2019 12th International Conference on Information & Communication Technology and System (ICTS), 291–295.
Wu, F. (2022). Intelligent Application of Data Mining Model in Chinese International Education. Mathematical Problems in Engineering, 2022, 1–7. https://doi.org/10.1155/2022/9171551
Wu, J. (2022). Intelligent Classroom Learning Model of College English Based on Data Mining Technology in Mobile Edge Computing Environment. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/4777195
Wu, X. (2022). The Collection and Utilization of Web Resources for Teaching World History Based on Data Mining Technology. Journal of Environmental and Public Health, 2022. https://doi.org/10.1155/2022/9124952
Xing, W., & Du, D. (2019). Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention. Journal of Educational Computing Research, 57(3), 547–570. https://doi.org/10.1177/0735633118757015
Yağcı, M. (2022a). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-022-00192-z
Yağcı, M. (2022b). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-022-00192-z
Yakubu, M. N., & Abubakar, A. M. (2021). Applying machine learning approach to predict students’ performance in higher educational institutions. Kybernetes, June. https://doi.org/10.1108/K-12-2020-0865
Yang, L. (2021). Data Mining Based College English Teaching Assistant Expert System. Proceedings – 2021 6th International Conference on Smart Grid and Electrical Automation, ICSGEA 2021, 280–283. https://doi.org/10.1109/ICSGEA53208.2021.00068
Yang, T., Chen, B., Wang, W., & Li, S. (2022). Research on Feedback Service for Teaching Based on Educational Data Mining. Proceedings – 2022 International Conference on Machine Learning and Knowledge Engineering, MLKE 2022, 306–309. https://doi.org/10.1109/MLKE55170.2022.00065
Zhang, W., & Qin, S. (2018). A brief analysis of the key technologies and applications of educational data mining on online learning platform. 2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018, 83–86. https://doi.org/10.1109/ICBDA.2018.8367655
Zhao, X. (2022). Leveraging Data Mining Technique to Enhancing Online Education and Its Efficiency Study. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/4946619
Zhou, X., & Wu, X. (2022). Teaching Mode Based on Educational Big Data Mining and Digital Twins. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/9071944
Zhou, X., & Wu, X. (2022). Teaching Mode Based on Educational Big Data Mining and Digital Twins. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/9071944

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Systematic Review of Data Mining in Education on the Levels and Aspects of Education.

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Abstract

Figures

Introduction

Related Reviews on EDM

2.1 DM in Education

2.3 Levels of Education

2.3.1 Basic Education

2.3.2 Pre-Tertiary Education

2.3.3 Tertiary Education

2.4 Major Aspects of EDM.

2.4.1 Academic Performance

2.4.2 Teaching and Learning Resources

2.4.3 Learner Attributes

3.1 Literature Search Criteria

3.1.1 Conducting the Search

3.1.2 Search Strategy

3.2 Publication Selection

3.2.1 The Inclusion Criteria

3.2.2 Exclusion Criteria

3.2.3 Selecting Primary Sources

Presentation of Results

Discussion of Research Gap

5.1 Population Gap

5.2 Knowledge Gap

Conclusion

Recommendation

Declarations

References

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