Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review
Abstract
:1. Introduction
- What and who are the major journals publishing the AIME studies? What are the most cited papers of AIME research? Who are the most productive and cited authors of AIME research?
- What are the most used keywords of AIME research? What are the relationships between the keywords?
- What are the application domains of AIME research?
- What are the sample groups selected for AIME research?
- What are the research methods adopted in AIME research?
- What are the roles of AI in mathematics education?
- What are the adopted AI algorithms in AIME research?
- What are the research issues investigated in AIME research?
2. Literature Review
3. Method
3.1. The Article Selection Process
3.2. Data Coding and Analysis
- Application domains: by referring to Yang et al. [19], the application domains in mathematics education were categorized into general/ mathematics foundations, discrete mathematics /algebra, analysis, geometry/ topology and applied mathematics, others, non- specified and mixed.
- Research sample groups: by referring to Hsu et al. [35], the research sample groups in the literature were categorized into elementary school students, junior high school students, higher education students, teachers, mixed groups and non-specified.
- Research methods: by referring to the coding scheme of Hsu et al. [35], the research methods were divided into quantitative, qualitative and mixed methods.
- Roles of AI: as suggested by Zawacki-Richter et al. [16], the roles of AI in education include profiling and prediction, ITS, assessment and evaluation and adaptive systems and personalization.
- Adopted AI algorithms: by referring to the study of Hwang et al. [12], AI algorithms were categorized into evolutionary algorithms, Bayesian inferencing and networks, search and optimization, fuzzy set theory, deep learning, case-based reasoning, traditional machine learning approaches and knowledge elicitation methods via interviewing domain experts and mixed. Traditional machine learning approaches include statistical learning; data mining; or symbolic learning approaches, such as Item Response Theory (IRT), linear regression, polynomial regression, classification, clustering, Iterative Dichotomiser 3 (ID3), version space, support vector machines and neural networks.
- Research issues: by referring to Tu and Hwang [36], research issues were classified into cognitive, affect, skills, learning behaviors, correlation, relevance, system design or evaluating AI system/tool performance, meta-cognition and learning styles.
3.3. Data Coding and Analysis
4. Results
4.1. Main Journals, Most Cited Papers and Most Productive and Cited Authors
4.2. Most Used Keywords
4.3. Application Domains
4.4. Sample Groups
4.5. Research Methods
4.6. Roles of AI
4.7. Adopted AI Algorithms
4.8. Research Issues
5. Discussion
- The greatest amount of AIME research was published in Computers & Education, followed by the Journal of Educational Psychology and the Journal of Computer Assisted Learning. In addition, the top three most cited journals (co-citation analysis) are the Journal of Educational Psychology, Computers & Education and Learning and Instruction. That is, more education and educational technology researchers have engaged in AIME research than mathematics education researchers. This implies the need to encourage mathematics education researchers to consider using AI technology in their studies.
- From the results of using cluster analysis on author keywords, three clusters of AIME studies were found; that is, “AI-based learning systems”, “personalized/adaptive learning” and “learning strategies/models.” Moreover, a new and small cluster, EDM in mathematics education, was formed in recent years. This could be a good reference for those intending to conduct AIME research in the future.
- The most frequently adopted application for AIME studies was discrete mathematics/algebra, followed by general/foundations. On the other hand, geometry and topology, applied mathematics, mathematics literacy and across-disciplines (e.g., STEM) were seldom included in those AIME studies. This implies that AIME applications remain in the beginning stage; that is, researchers mainly focused on using AI technologies to solve fundamental problems in mathematics courses.
- The most frequently adopted sample group for AIME studies was junior high school students, followed by elementary school students and higher education students. On the other hand, teachers and senior high school students were seldom adopted by AIME research. This could be due to the fact that learning mathematics in junior high school is more challenging than in elementary school. Therefore, junior high school students need more assistance to face the challenge. Moreover, choosing elementary school students and higher education students could be due to convenience. Elementary school teachers generally tend to accept new learning approaches, since they need not worry about students’ entrance examinations, in particular, in Asian countries. Choosing higher education as the sample groups is also a convenient selection, since most of the authors were researchers in universities. Similarly, most studies focused on students’ learning performance, since it is the main objective for all levels of mathematics education.
- Quantitative methods were the most frequently adopted approaches, followed by mixed methods. This is reasonable, since most studies aimed to evaluate students’ learning performance via analyzing their test scores as well as learning attitudes or attitudes via questionnaires.
- The most frequent role played by AI in mathematics education was “intelligent tutoring systems”, followed by “profiling and prediction” and “adaptive systems and personalization.” This is consistent with the finding regarding the research issue, that is, evaluating students’ learning performance is the main focus of AIME studies. The main purpose of developing ITS is to evaluate students’ learning problems and to provide instant supports to them, which aims to improve their learning performances. Although adaptive learning systems and personalization have the same aim, developing such adaptive learning systems is more challenging, and hence the number of such studies is relatively small.
- Most studies adopted the traditional machine learning approach, or knowledge elicitation methods via interviewing domain experts, while modern AI approaches, such as deep learning, were seldom adopted. This could be due to the fact that those AIME studies mainly focus on the development of ITS for evaluating individual students’ learning statuses to provide assistance to them. This objective is highly related to features of traditional machine learning approaches (e.g., statistical learning, data mining and decision trees) and knowledge elicitation methods via interviewing domain experts; that is, domain knowledge is explicitly represented and used for decision making or prediction [7,12].
- Most AIME studies investigated students’ learning achievements (cognition dimension), and learning motivation and attitude (affect dimension). This is because the objective of mathematics education is to foster students’ cognition competences. Moreover, since mathematics courses are generally considered by students as being challenging, investigating students’ learning motivation or attitude is hence an important research focus. It is also reasonable that “skill” was seldom discussed, since it is less relevant to the objectives of mathematics education.
6. Conclusions
- It is suggested that researchers consider using AI applications to provide students with personalized guidance or support, and to investigate the impacts of AI-based learning approaches in mathematics education research.
- It would be innovative to use EDM to investigate the factors affecting students’ learning outcomes and to find associations between students’ learning behaviors and performances.
- It could be valuable to adopt relevant AI applications in learning activities of advanced mathematics programs, such as geometry and topology, applied mathematics, mathematics literacy and cross-disciplinary (e.g., STEM) courses.
- It is important to consider how AI applications benefit those seldom-adopted sample groups in mathematics education, such as teachers and senior high school students.
- In addition to quantitative analysis, it is important to encourage researchers to conduct qualitative methods to collect learners’ feedback on AI-supported mathematics learning and to analyze learners’ perceptions in depth.
- It would be valuable to develop adaptive mathematics learning environments via the collaboration of mathematics education, educational technologies and computer science researchers.
- It could be interesting to employ modern AI technologies, such as deep learning, in mathematics education. Although the related AI applications, such as image recognition and voice recognition, might be directly relevant to mathematics content, they can benefit learners from other perspectives, such as providing visually impaired students with a supportive interface.
- It is important to investigate the effectiveness of using AI in mathematics learning activities from different perspectives by taking rarely considered research foci into account, such as cognitive load, collaboration and communication competences and learning anxiety.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Rank | Title | Journal | Authors, Year | Total # of Citations |
---|---|---|---|---|
1 | Are badges useful in education?: it depends upon the type of badge and expertise of learner | Educational Technology Research and Development | Abramovich, Schunn and Higashi, 2013 | 154 |
2 | Using Adaptive Learning Technologies to Personalize Instruction to Student Interests: The Impact of Relevant Contexts on Performance and Learning Outcomes | Journal of Educational Psychology | Walkington, 2013 | 65 |
3 | Diagnosing student learning problems based on historical assessment records | Innovations in Education and Teaching International | Hwang, Tseng and Hwang, 2008 | 39 |
Author | Countries/Areas | Publications | Total # of Citations (Citations Per Paper) |
---|---|---|---|
Xiangen Hu | USA | 3 | 49 (16.33) |
Candace Walkington | USA | 3 | 33 (11) |
Gwo-Jen Hwang | Taiwan | 2 | 48 (24) |
Scotty D. Craig | USA | 2 | 40 (20) |
Vincent Aleven | USA | 2 | 22 (11) |
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Hwang, G.-J.; Tu, Y.-F. Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review. Mathematics 2021, 9, 584. https://doi.org/10.3390/math9060584
Hwang G-J, Tu Y-F. Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review. Mathematics. 2021; 9(6):584. https://doi.org/10.3390/math9060584
Chicago/Turabian StyleHwang, Gwo-Jen, and Yun-Fang Tu. 2021. "Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review" Mathematics 9, no. 6: 584. https://doi.org/10.3390/math9060584