Machine Learning in the Teaching Quality of University Teachers: Systematic Review of the Literature 2014–2024

Abstract

The growth in the number of students in higher education institutions (HEIs) in Latin America reached 33.5 million in 2021 and more than 220 million worldwide, increasing the number of data volumes in academic management systems. Some of the difficulties that universities face are providing high-quality education to students and developing systems to evaluate the performance of teachers, which encourages offering a better quality of teaching in universities; in this sense, machine learning emerges with great potential in education. This literature review aims to analyze the factors, machine learning algorithms, challenges, and limitations most used to evaluate the quality of teaching based on performance. The methodology used is PRISMA, which considers analyzing literature produced between 2014 and 2024 on factors, prediction algorithms, challenges, and limitations to predict the quality of teaching. Here, 54 articles from journals indexed in the Web of Science and Scopus databases were selected, and 111 factors were identified and categorized into five dimensions: teacher attitude, teaching method, didactic content, teaching effect, and teacher achievements. Regarding the advances in machine learning in predicting teacher teaching quality, 30 ML algorithms were identified, the most used being the Back Propagation (BP) neural network and support vector machines (SVM). The challenges and limitations identified in 14 studies related to HEIs are managing the large volume of data and how to use it to improve the quality of education.

1. Introduction

Artificial intelligence (AI) and machine learning (ML) techniques are effective sources for gaining valuable insights into teaching quality (TQ) and have been widely applied in various fields such as telecommunications, insurance, retail, construction, transportation, healthcare, finance, and education [1,2,3,4,5]. The application of AI and ML will play an increasingly important role in higher education institutions (HEIs) because it enables students and faculty to have a personalized approach to faculty teaching quality based on their own experiences and preferences [6]. At the same time, AI can reduce the time needed for routine administrative tasks, allowing HEI faculty to focus more on teaching and research [7].

In recent years, improving the quality of teaching and learning has been considered an important concern in research and practice; the enormous amount of data accumulated in HEIs is a gold mine where useful knowledge for educational quality can be discovered [8]. Based on the above, the report of [9] indicates a high growth of undergraduate and graduate students in HEIs in Latin America, reaching 33.5 million in 2021, experiencing significant growth. At a global level, according to data from the UNESCO Institute for Statistics, the number of students enrolled in HEIs is more than 220 million; it has more than doubled in recent decades [10]. The study of [11] indicates that evaluating TQ accurately and efficiently has become a priority for educational institutions because they seek to ensure academic excellence and remain competitive in an increasingly demanding environment. In this context, the advancement of technology, and in particular, artificial intelligence and machine learning, offers new opportunities to improve the assessment of the teaching quality of university teachers (TQUT). In addition, the global AI market in the education sector reached a value of 4 billion dollars in 2022 and is projected to grow at a compound annual growth rate (CAGR) of over 10% between 2023 and 2032, which shows the increasing adoption of AI solutions in education [12].

The quality of teaching naïven universities is a key indicator of an institution’s educational level and is fundamental for students’ academic and professional development [13,14]. Its assessment of teaching quality is a complex problem with multiple criteria and variables, which requires a combination of quantitative and qualitative methods to be more objective and scientific [15,16]. Furthermore, Ref. [14] indicates that an accurate and objective evaluation allows teachers to improve their teaching strategies, optimize educational resources, and ensure that students receive an education aligned with standards of excellence. The topic of teaching quality has been widely debated in academic literature, as evidenced in the studies of [17,18,19], which propose a TQ evaluation model in BP adaptive neural networks. Ref. [20] proposes a quality framework based on ML. Refs. [21,22] design a TQ evaluation model in English. Ref. [23] presents methods for evaluating and analyzing university teaching quality. Ref. [24] proposes a portfolio evaluation of teaching effectiveness in ML. Ref. [25] presents an online TQ assessment model. Ref. [15] proposes an intelligent CE assessment model based on ISSA DRNN. These studies highlight the importance of integrating AI and ML for data-driven decision-making.

The issue explored in this article focuses on the quality of the teacher’s teaching (QTT) and measures the impact of teacher effectiveness (TE) on performance within higher education institutions (HEIs). According to [26], discovering university professors’ key skills and cultivating their capabilities is both a primary task and a challenging problem for HEIs. Despite universities striving to recruit and train teachers with a broad range of competencies, there is no clear, validated system to comprehensively identify and measure these skills. As noted in [27,28], ensuring the quality of subject-specific teaching scientifically relies on recommending appropriate teachers—a complex issue due to the diverse characteristics of faculty, necessitating the design of effective algorithms. Furthermore, Ref. [17] highlights that evaluating teaching quality in universities is critical because it significantly affects the reputation of these institutions, which is largely determined by the quality of education. However, this evaluation is complex due to the non-linear nature of the problem and the subjectivity involved. Assessing the quality of a product is far easier than evaluating the instruction of a professor, given the various components and the bidirectional nature of the teaching process, which involves both teachers and students.

According to [29], AI techniques benefit students, professors, directors, and decision-makers, enabling them to perform tasks more effectively and efficiently while enhancing educational quality. However, as [20] points out, traditional evaluation methods—such as direct observations and administrator-led assessments—are often subjective and fail to accurately reflect a teacher’s impact on student learning. This lack of precision and objectivity limits the ability of institutions to identify and retain the most effective teachers. Moreover, as indicated, traditional methods struggle to process large volumes of information effectively, applying machine learning (ML) algorithms.

Evaluating teaching quality in higher education (TQUT) helps institutions understand faculty performance, promptly identify shortcomings, and improve faculty quality and university-level education [23,30,31]. However, as [32,33] points out, current teacher performance evaluation systems are inefficient, making it challenging to assess faculty performance accurately. Additionally, ineffective talent acquisition mechanisms and limited human resource mobility hinder universities’ ability to manage their personnel efficiently, ultimately negatively impacting educational quality and institutional development.

Research has sought to identify the factors or indicators of teaching quality and teacher performance. For instance, Ref. [23] used 16 evaluation indicators classified into four categories, selected according to five principles, to evaluate teaching quality in universities offering diverse academic programs. Similarly, Ref. [21] applied 25 indicators across eight categories and noted high correlations among the variables. Ref. [18] identified 16 teaching quality evaluation indicators across four aspects or categories, constructing a comprehensive evaluation index system. Meanwhile, Ref. [34] identified 24 teaching quality indicators in English, grouped into five categories. These findings underscore the need to compile a catalog of TQUT factors systematically.

Regarding the prediction algorithms, different machine learning algorithms have been used in teacher teaching quality, such as the model of [11] that applies a teaching quality evaluation model based on a backpropagation (BP) neural network that solves the deficiencies of teaching quality evaluation methods and improves the accuracy of the evaluation prediction. For his part, Zhong [23], in his ML research, selected the support vector machine (SVM) algorithm to evaluate teaching quality, where the accuracy of the SVM algorithm ranged between 80 and 98%. The study [20] applied multiple linear regression to assess teaching effectiveness and predict teacher performance with a limited sample of 400 data sets, obtaining 95.77% accuracy. In addition, Ref. [21] applies the combination of embedded web systems and machine learning using the fuzzy decision tree algorithm to evaluate the quality of online teaching in English, significantly improving the quality of education. Each author indicates that their models should be tested with new indicators and a more extensive data set.

According to the challenges and limitations [35], one of the most significant challenges of higher education institutions is the accumulated amount of data and how it can be used to improve the quality of academic programs. Almufarreh [20] indicates that it should be contrasted with other ML to verify its accuracy. According to [23], a significant challenge is selecting scientific, complete, measurable, oriented, and valid evaluation indicators to ensure that teaching performance is adequately evaluated in different aspects. Ref. [36] indicates that one of the limitations is that teacher competence is a relatively complex concept; it is necessary to dynamically adjust the indicators and weights of the evaluation system according to the specific subject areas and teacher training requirements, and a limited number of experts can affect the evaluation result, so the evaluation system must be tested and improved. Thus, the need to study its possible impact and limitations arises.

However, studies in this field have revealed that much knowledge needs to be inventoried, analyzed, and classified for factors and algorithms. Despite advances in teacher education quality, there are significant gaps in knowledge, particularly in implementing ML-based techniques that can effectively handle variability in data to understand its implications in TQ better. For these reasons, a systematic literature review was conducted to map the research in this area and identify gaps in knowledge in the factors, algorithms, challenges, and limitations of ML in TQUT.

This study aims to systematically review all the important aspects developed and related to ML prediction factors, algorithms, trends, and limitations based on ML. To answer the research question ¿What ML prediction factors, advances, challenges, and limitations have been investigated about TQUT?

The main contributions of this review are the following:

∘

To provide a comprehensive catalog of teaching quality factors classified into five dimensions. Its application will allow you to build a factor framework with the different variables or organize your data set to group them by category.

∘

To identify the various prediction algorithms used and their accuracy in TQUT. To measure teacher performance and obtain better accuracy in the algorithms used in the data set.

∘

To compile the challenges and limitations identified in the different studies contributing to TQUT. It will allow HEIs to know the computational power they need according to their data volumes. It will also allow them to understand the structure of the different data sets and the importance of selecting the indicators.

∘

To provide the reader with a wide range of bibliographical references that they can use to deepen their understanding of ML-based models that facilitate TQUT prediction. This will allow each researcher to know the identified barriers and previous studies to address new research.

The contribution of this paper lies in its systematic analysis of how AI is transforming higher education through advanced technologies such as machine learning algorithms, predictive analytics, and supervised and unsupervised learning techniques. These tools are critical to creating educational solutions customized for decision-making. ML integration addresses the challenges of improving teaching quality and identifying improvement patterns according to the category of factors in performance evaluation. The insights provided by this study aim to review all the important aspects developed and related to factors, ML prediction algorithms, trends, and limitations based on ML. In addition, it guides teachers, HEI managers, and researchers in leveraging ML-driven innovations to improve academic success and institutional effectiveness.

This article is organized into six sections. Section 2 provides a background on TQUT and ML. Section 3 outlines the methodology for the systematic literature review. Section 4 presents the results, focused on answering the research questions discussed in Section 5. Finally, the conclusions follow in Section 6.

2. Theoretical Background

According to Gu [11], competition in the 21st century has to do with the quality of teachers, and the challenges of all countries in the world also have to do with the quality of talent. As [37] indicated, the highest quality level in the educational system is to predict teachers’ performance to obtain knowledge about the educational elements affected. The work of Abunasser et al. [35] mentions that one of the greatest challenges of HEIs is the accumulated amount of data and how it can be used to improve the quality of academic programs and teacher performance. In addition, Ref. [38] indicates that an adequate allocation of academic resources can support the management of HEIs.

This section assesses the theoretical foundations of ML and QTT in higher education and explores their integration into academic practices. Through a detailed analysis of these foundations, the transformative potential of ML in HEIs to reconfigure educational ecosystems and improve teaching practices and institutional decision-making becomes evident.

2.1. Teachers’ Teaching Quality

The quality of teachers’ teaching is essential for a university and college to survive and grow and is a measure of the trend towards international higher education [3]. According to [39], teaching quality is not defined by ranking a teacher’s competence or effectiveness against others. Still, it is defined by the teacher’s role to be reflective, effective, collaborative, and continue learning. For Chen and Mokhtar [40], the quality of teachers’ teaching is assessed through four channels: student assessment, expert assessment, peer assessment, and teacher self-assessment. However, for Adams et al. [41], evaluating teaching quality helps teachers understand the quality of their work and discover deficiencies in time.

Effective teachers are firmly committed to creating a positive learning experience and impacting student learning to transform it into a quality environment for knowledge dissemination [24]. Therefore, Ref. [42] indicates the importance of having a well-defined teacher profile and well-prepared educators to use advanced technologies, improving the quality of teaching and student learning outcomes.

2.2. Teacher Performance Assessment

Teacher assessment in higher education is crucial to ensure that students receive high-quality education and that teachers can deliver their students the most excellent learning experience possible [20]. Assessing teacher quality helps to understand teaching effectiveness, identify areas for improvement, and provide effective feedback, which can improve teaching efficiency and university management [23,43,44,45].

Therefore, Refs. [46,47] mention that teacher evaluation is an education management tool to facilitate the teacher’s personal growth. In addition, Meng et al. [48] state that the evaluation of classroom performance is one of the important components of a comprehensive evaluation of the quality of university education and has aroused the interest of both universities and teachers. As indicated by Zhang et al. [49], the teaching quality evaluation systems must be synchronized in universities; the current situation makes it necessary to build a teaching quality evaluation system; its positioning determines for teachers that the evaluation of teaching quality cannot be done only through theory, and smart technology must be used.

2.3. Artificial Intelligence

AI was first used by McCarty, Minsky, Rochester, and Shannon from 1996 to 2006. Popenici and Kerr [50,51] and Brooks and Thompson [52] agree that AI is a computer system capable of performing human-like processes, such as learning, adaptation, synthesis, self-correction, and the use of data for complex processing tasks. Therefore, Lee M. et al. [53] mention that AI evolves as machines become increasingly capable of performing tasks that require intelligence, so much so that, during the 1990s with the introduction of computers, according to [29] in the educational sector, improvements have been made focused on the development of AI-enhanced learning environments, intelligent tutoring systems, adaptive learning systems, intelligent agents, and intelligent collaborative learning systems.

2.4. Machine Learning Algorithms

ML for Alloghani et al. [54] is an area of AI that uses computerized techniques to solve problems based on data and historical information that does not require modifications to the core process. Refs. [50,55,56] agree that ML is a computer algorithm that learns from data to make decisions without being programmed. Therefore, Ref. [35] mentions that AI algorithms improve with use and become more accurate when more data are available. On the other hand, machine learning systems, according to [57], are used in education to predict students’ learning performance and produce personalized learning paths.

With the continuous advancement of information technology and machine learning in today’s culture, it is possible to employ machine learning to create a teaching evaluation system, which can be used to monitor teachers’ teaching in real time [3]. As a result, teachers can get to know themselves more quickly in the teaching process and adjust their teaching plans.

2.5. Related Works

Seven literature reviews have been identified for using AI and ML. They focus on different fields of education, such as student performance, student dropout, at-risk students, learning processes, efficient management of educational resources, and big data in teacher evaluation. None of them focus on teacher teaching quality evaluation and teacher performance in ML. These studies differ in terms of the methodology used and the approaches adopted; key findings and a description of each study are provided in Table 1.

Table 1. Research gaps in AI and ML academic topics.

Authors	Key Findings or Research Gaps	Article Description
[29]	- AI applications help address major educational challenges, such as identifying at-risk students. - Main uses of AI in education: predictive modeling, intelligent analysis, assisted technology, automatic content analysis, and image analysis. - AI has been beneficial in improving education quality in Latin America. - Research gaps: limited geographic coverage, concentrated in a few Latin American countries, and lack of comprehensive studies on ethical and privacy implications of AI in education. - There is a lack of studies that comprehensively address the ethical and privacy effects associated with the application of AI in education.	Year: 2016–2021 Systematic Literature Review Analyzes 31 publications. Databases: Web of Science, IEEE Xplorer, Scielo, CAPES Portal.
[58]	- Academic and demographic characteristics are the main influencers of student performance. - Decision trees and classification techniques are the most commonly used methods. - Research gaps are revealed due to the lack of studies on basic academic performance and the prescription of intervention plans to prevent low performance.	Year: 2016–2022 Systematic Literature Review Analyzes 84 publications. Databases: IEEE Explorer, ERIC, Science Direct, Springer Link.
[59]	- Classification models are the most used for identifying student performance. - Most common techniques: Random Forest,naïvee Bayes, Support Vector Machines, Decision Trees, and Boosting Algorithms. - Research is concentrated geographically in the U.S., China, and India.	Year: 2010–2020 Systematic Literature Review Analyzes 89 publications. The databases are IEEE Explorer, ERIC, Science Direct, Scopus, Web of Science, EBSCO, Emerald Insight, Education Database, and Taylor and Francis.
[60]	- Various machine-learning techniques are used to understand and address underlying challenges and predict at-risk students and dropout rates, thereby improving student performance. - Authors highlight the essential role of ML methods in predicting at-risk students. - Research is concentrated in a few countries.	Year: 2009–2021 Systematic Literature Review Analyzes 78 publications. Databases: IEEE Explorer Digital Library, ResearchGate, Scopus, Springer Link, Association for Computing Machinery, Directory of Open Access Journals.
[61]	- Machine learning enables more robust analysis of large datasets in education, surpassing traditional methods in handling complexity and non-linearity. - Despite its potential, ML adoption in educational sciences is limited but growing. - Philosophical changes in model evaluation are needed to fully leverage ML’s advantages in this field.	No information about the year range, databases used, or the number of publications analyzed is available.
[62]	- AI has proven effective in personalizing learning and managing educational resources efficiently. - Significant advancements in AI for evaluation and administrative support in educational settings. - Research gaps: lack of comprehensive studies addressing ethical risks and challenges of AI implementation - Long-term impact studies on educational outcomes are needed.	No information about the year range, databases used, or the number of publications analyzed is available.
[63]	- Growing trend in using big data for teacher evaluation. - Current systems focus mainly on teaching processes without addressing talent training goals. - Proposes an innovative evaluation system integrating multiple assessments and a closed feedback loop. - Lack of focus on talent training outcomes in existing evaluation systems.	Year: 1991–2021 Bibliometric Review The search yielded 1125 articles referencing 47 publications. Databases: Web of Science.

Issah [58] conducted a literature review on applying machine learning techniques to predict academic performance using academic and non-academic factors to identify the machine learning methods used and how student characteristics relate to their performance. Fahd et al. [59] conducted a meta-analytic analysis of previous studies on the application of machine learning in higher education to assess academic performance, identify at-risk students, and predict dropout, using the PRISMA framework to systematize the review of the studies.

In their article, Albreiki [60] presents a systematic literature review on predicting student performance using ML techniques. The review focuses on identifying student risk factors and predicting school dropout using various ML techniques. Salas-Pilco [29] investigates AI’s application in Latin America’s higher education, focusing on how AI applications are used in learning, teaching, and administration processes within HEIs. The review covers AI techniques, including machine learning, deep learning, and natural language processing, and examines the main educational topics these applications address.

Hilbert [61] examines the use of machine learning techniques in educational sciences and highlights how these techniques can analyze large data sets to model complex relationships, which is crucial given the increasing availability of large-scale educational data through massive open online courses and other means. Ref. [62] addresses the use of AI techniques in improving educational quality, highlighting recent challenges and progress in this field. It focuses on how AI applications can be used in education to improve educational services and overcome existing challenges, including integrating AI into different areas of education. Ref. [63] conducts a bibliometric review of the teaching knowledge assessment system in universities in the context of big data, examines current trends and key research points in the field, reviews conventional teaching assessment models, and proposes directions for improving teaching knowledge assessment systems.

Therefore, despite literature review studies (as shown in Table 1), a comprehensive analysis that considers the existing articles on ML implementation in HEIs regarding teacher quality, teaching quality, or teaching performance could not be found. This study aims to provide a systematic literature review of machine learning models’ application, teaching quality factors, and trends and limitations in the higher education sector. Therefore, this research attempts to take initial steps to fill the existing gap in the literature. It classifies and analyzes the approach of applying machine learning studies on teacher quality, factors, and algorithms employed in higher education. Furthermore, the research briefly identifies future research trends.

3. Materials and Methods

The systematic review for the development of this research is based on the methodology “The PRISMA 2020 statement” [64], and the procedure proposed by [65] was used, which was adapted by [66] and has the following phases:

• Planning: Research questions, search protocols, and keywords are proposed.
• Development: Documents corresponding to primary studies are selected according to the inclusion and exclusion criteria and the protocol established in the planning phase.
• Results: The review and statistical analysis results are presented in Section 3.3 and Section 4.

3.1. Planning

To carry out the bibliographic review, three research questions were formulated, which will allow us to identify the aspects that have been analyzed on factors, techniques, challenges, and limitations of TQUT:

RQ1: What are the factors considered in teacher teaching quality in higher education?

RQ2: What are the advances of ML in predicting teacher teaching quality in higher education?

RQ3: What challenges and limitations have been identified in the application of ML for teacher teaching quality in higher education?

To answer the research questions, a review of primary publications was conducted to identify potentially relevant documents. Searches were conducted in bibliographic databases from January 2014 to December 2024: “title-abs-key” for Scopus and “topic” for Web of Science (Wos), using the following search string as shown in Table 2.

Table 2. Database search string.

Database	Search String
Scopus	TITLE-ABS-KEY (“teaching quality” OR “quality education” OR “quality of teachers” OR “teacher performance” OR “teacher assessment” OR “teacher quality” OR “teacher evaluation” OR “teachers effectiveness”) AND (models OR factors OR techniques OR algorithms OR “decision making” OR prediction OR “higher education” OR framework) AND (“machine learning” OR “artificial intelligence” OR “recommender system”)
Wos	Results for (“teaching quality” OR “quality education” OR “quality of teachers” OR “teacher performance” OR “teacher assessment” OR “teacher quality” OR “teacher evaluation” OR “teachers effectiveness”) AND (models OR factors OR techniques OR algorithms OR “decision making” OR prediction OR “higher education” OR framework) AND (“machine learning” OR “artificial intelligence” OR “recommender system”) (Topic)

The last 10 years, considering that in the previous years, there were few studies and related works, including reviews that began in 1991 and others since 2009, were also considered to be periods of technological progress and that in recent years, machine learning has been evolving in its application to the teaching of university teachers.

The search was limited to publications with an impact factor from the SCImago journal ranking. The research questions formulated served as a guide throughout the review process. In addition, inclusion and exclusion criteria were defined to identify relevant studies for the research, which are presented in Table 3.

Table 3. Inclusion and exclusion criteria.

Inclusion Criteria	Exclusion Criteria
CI1: Studies that answer at least one of the research questions. CI2: Primary studies CI3: Studies from 2014 to 2024. CI4: Studies that apply artificial intelligence or machine learning in education.	CE1: Studies focused on business, philosophical, and social issues. CE2: Studies that do not mention using artificial intelligence or machine learning in education. CE3: Studies from the preschool and school context. CE4: Studies after 2014

3.2. Development

The primary analysis process of the studies found during the search was subjected to a selection procedure based on the criteria detailed in Table 3, covering both the inclusion and exclusion criteria. To achieve this, it was necessary to conduct a prior review of the content to determine its relevance for the present study and to find those studies related to the factors, techniques, challenges, and limitations of TQUT through ML. Most of the works were discarded because they corresponded to topics such as prediction in academic performance and educational competencies in business areas, health or agriculture, and school educational levels, did not apply machine learning, and others for not contributing to the research questions. Figure 1 illustrates the PRISMA flowchart explaining the applied process and identifying the activities carried out to select or reject the studies. Mendeley was used to organize and manage the bibliographic references throughout the review.

Figure 1. Systematic review process according to PRISMA [64].

3.3. Data Collection and Analysis

For data collection, relevant publications were identified and studied. The search covered the topics of teacher teaching quality, teacher performance evaluation, and teacher quality, which guaranteed comprehensive coverage based on machine learning in higher education. For the analysis of the articles that passed the abstract selection stage, they were reviewed in their entirety to ensure that they presented empirical findings, theoretical advances, and the application of ML and that they answered the research questions. We had the help of education experts who allowed us to identify the different variables of the factors and thus be able to merge similar factors or define their names. This focused search strategy represented recent advances in ML, particularly in its application in higher education.

3.3.1. Publication Trend by Year

The number of publications in the aspects of factors, algorithms, challenges and limitations in TQUT shows an increasing trend in both potential articles (see Figure 2a) and selected articles (see Figure 2b). Which highlights the greater importance of research on the quality of teaching.

Figure 2. Number of publications per year: (a) potentially eligible and (b) selected studies.

3.3.2. Articles Selected by Journal Quality Factor

Of the total number of articles selected, 68.52% are in the Q1 and Q2 quartiles, which indicates a great interest in TQUT and guarantees that the results obtained in this research are reliable (see Figure 3).

Figure 3. Number of articles published by quartile.

3.3.3. Selected Articles by Journal

Figure 4 illustrates that the three most prominent journals (IEEE Access, Mobile Information Systems, and Scientific Programming) accounted for 27.78% of the publications; the “Other” category was classified as journals that contributed only one article.

Figure 4. Articles by journal.

4. Results

In this section, we answer the research questions raised in Section 3.1 based on the selected studies.

4.1. RQ1: What Are the Factors Considered in Teacher Teaching Quality in Higher Education?

The factors of teacher quality are those criteria and characteristics that are considered to ensure that teachers possess the necessary qualities, skills, and knowledge. Authors such as [19] indicate that the dynamic process of teaching and learning constitutes a teaching activity, and many factors can affect the quality of teacher education to different degrees and angles. In this context, 43 studies were found describing 112 factors used for TQUT, which have been grouped into 5 dimensions described in Table 4.

Table 4. Category of Factors used for TQUT.

Id	Category	References
CF1	Teacher’s Attitude	[11,14,18,21,23,47,67,68,69,70]
CF2	Teaching Method	[11,14,21,23,67,68,70,71]
CF3	Didactic Content	[11,14,18,21,23,68,70,71]
CF4	Teaching Effect	[11,14,21,23,44,68,69,70,71]
CF5	Teacher’s achievements	[18,67]

Teacher attitude: 28 factors were identified, of which the most studied are effective communication, teacher-student interaction, teacher preparation before class, subject matter expertise, and teacher enthusiasm, respectively (see Table 5).

Table 5. Teacher attitude factors for TQUT.

Id	Factor	Description	References
AD1	Teacher-Student Interaction	The teacher and students maintain effective interaction during the course, contributing to the learning environment and academic development.	[5,18,31,35,36,37,41,47,72,73,74]
AD2	Teacher Preparation Before Classes	The teacher is well-prepared to teach the content before classes and carefully plans the lessons.	[5,18,23,30,45,69,70,72]
AD3	Effective Communication.	The use of language is clear, fluent, precise, and concise, adhering to standard norms.	[11,23,67,70,71,75]
AD4	Teacher Enthusiasm	The energy, passion, and motivation that a teacher shows when teaching his classes in the courses with a positive attitude.	[11,55,68,71,75,76]
AD5	Subject Matter Expertise	The teacher has solid professional knowledge of the topics taught has mastery of the subject, and provides a broader context to the students.	[14,24,31,73,75,77]
AD6	Teaching Style	Techniques and methods a teacher uses to present content, impart knowledge, and facilitate learning.	[27,28,37,67,69]
AD7	Classroom Discipline	The teacher manages student behavior to create a conducive learning environment.	[34,37,71,76,78]
AD8	Professional Ethics	The teacher handles academic and disciplinary aspects with impartiality, commitment, and ethical conduct, addressing aspects like confidentiality, professionalism, seriousness, and responsibility.	[14,30,46,68,76]
AD9	Reasonable Task Assignments	Serious guidance, an adequate amount of tasks, and timely correction of submitted tasks.	[45,69,70,74]
AD10	Clarity and precision of teaching content.	Clarity in delivering course content that is didactic, concise, organized, and standardized.	[67,74,77,79]
AD11	Organizational Skills	The teacher’s ability to plan, organize, and efficiently manage time, resources, and teaching activities within the course.	[48,74,76,79]
AD12	Practical Materials	Well-written boards and practical materials for classroom use.	[23,34,72]
AD13	Leadership	The teacher’s ability to lead projects and foster a positive and proactive learning environment.	[42,69,70]
AD14	Passion for Education	The teacher teaches by example, with love and dedication.	[23,37,75]
AD15	Punctuality in Classes	The teacher is punctual for classes and completes the course’s didactic content on time.	[18,45]
AD16	Practical Skills	The teacher’s ability to design and execute practical activities, labs, simulations, and projects reflecting real-world challenges	[68,79]
AD17	Teacher Characteristics	Personal and professional traits a teacher possesses that directly influence their effectiveness and performance in the classroom.	[27,28]
AD18	Teacher Strength	Personal and professional qualities of the teacher that contribute to their effectiveness and resilience in the classroom.	[21,75]
AD19	Respect for students	The teacher’s attitude toward students should be strict, respectful, and kind.	[14,18]
AD20	Morality	The conduct of the teacher and students should be exemplary, promoting an environment of mutual respect and integrity in the classroom.	[33,68]
AD21	Technical Skills	The teacher’s ability to select appropriate technologies for various learning objectives to enhance classroom interaction.	[26,75]
AD22	Personal Skills	Interpersonal and emotional skills the teacher brings to the learning environment, such as empathy, effective communication, patience, and the ability to motivate and inspire students.	[48,75]
AD23	Accessible and patient post-class advice	Teacher’s willingness to provide counseling to students outside of class hours, demonstrating patience and commitment to their learning.	[76,77]
AD24	Standards	Evaluates how the teacher applies standards and maintains quality criteria and performance expectations in their teaching and students’ learning.	[11]
AD25	Tools	The teacher’s ability to select instruments appropriate for the course objectives and students’ learning needs.	[11]
AD26	Student Attitude	Students’ behavior toward the learning process and their educational environment.	[46]
AD27	Basic Teaching Skills	Measures the effectiveness of basic teaching techniques used to facilitate student learning.	[38]
AD28	Growth Mindset	Reflects the belief that skills and intelligence can be developed with effort and dedication, promoting the continuous development of both the teacher and the students.	[75]

Teaching Method: 18 factors were identified, of which the five most studied are teaching resources, classroom environment, flexible teaching method, modern teaching method, and capacity for innovation (see Table 6).

Table 6. Teaching method factors for TQUT.

Id	Factor	Description	References
ME1	Flexible Teaching Method	The teaching method is flexible and diverse, stimulates student participation, facilitates two-way communication, and promotes innovative thinking.	[5,30,45,47,67,70,71,74,75,80]
ME2	Innovation Capacity	Teacher’s ability to introduce new ideas, methods, and technologies in their teaching practice	[11,14,26,42,49,75,77]
ME3	Classroom Environment	An active classroom environment, focused on communication and interaction with students, with good classroom order.	[21,23,37,43,45,67]
ME4	Teaching Resources	Active use of diverse teaching materials and educational resources.	[15,20,23,26,78,81]
ME5	Modern Teaching Method	The modern teaching method is reasonably adopted, ensuring high teacher efficiency and vivid lecture delivery.	[5,45,67,72]
ME6	Teaching Process	The educator’s ability to foster an interactive and participatory environment motivates students to engage in their learning process actively.	[15,18,44,76]
ME7	Course Characteristics	The teaching method should be selected based on the course’s characteristics, with a student-centered approach to learning and teaching.	[8,27,67,77]
ME8	Teaching Planning Skills	The teacher’s ability to strategically design and organize course content, learning activities, and assessments.	[30,40,47,78]
ME9	Teaching Strategies	The teacher’s ability to apply multiple approaches and employ teaching strategies that transmit knowledge and allow students to apply what they have learned in practical and real-life contexts.	[18,75,77,80],
ME10	Efficient Classroom Management	The teacher demonstrates good teaching management and efficient use of classroom time	[15,37,70]
ME11	Pedagogical Skills	Abilities and techniques employed by an educator to teach and facilitate learning effectively and efficiently.	[24,75]
ME12	Educational Technologies	Ability to scientifically utilize modern teaching tools.	[23]
ME13	Vitality	The educator’s capacity to deliver lessons with a positive attitude, enthusiasm, dynamism, and energy enhances their teaching style.	[11]
ME14	Reality	The teacher’s ability to connect course content with real-world situations makes learning relevant and applicable for students.	[11]
ME15	Teaching Skills	Skills to adapt teaching to changing classroom conditions and needs, alongside mastery of the subject content.	[30]
ME16	Analytical Approach	Teaching is well-detailed, highlighting important and challenging points.	[23]
ME17	Logical and Organized	Classroom design should balance theory and practice, achieving a layered and in-depth effect.	[18]
ME18	Focus on individuality	Teacher’s ability to recognize and respond to students’ individual differences, adjusting their teaching according to their needs and abilities.	[14]

Teaching content: Twenty factors were identified, of which the five most studied are teaching content, combining theory with practice, curricular planning, well-organized content, and the level of difficulty of the subject (see Table 7).

Table 7. Teaching content factors for TQUT.

Id	Factor	Description	References
CD1	Combines Theory with Practice	The teacher combines theoretical concepts with practical activities in the study content to enhance students’ understanding and skills.	[5,14,18,23,30,45,70,72,75,76,79]
CD2	Teaching Content	The teaching content is enriched, the pace is reasonable, it reflects academic progress, facilitates understanding, and promotes active and meaningful learning among students.	[5,23,36,40,68,71,74]
CD3	Well-Organized Content	Clear, organized, and logical structure in the presentation of the course’s teaching content, aligning with the syllabus.	[5,15,18,72,76,79]
CD4	Curriculum Planning	Organization and structuring of educational content by the teacher, consistent with the syllabus and course plan.	[5,23,37,73,75]
CD5	Subject Difficulty Level	Evaluation of the content and structure of the subject’s difficulty level for students.	[13,24,27,28,35]
CD6	Teaching Process	The teaching objectives are clear, the content is scientific, the concepts are precise, and the examples are appropriate.	[26,40,45,70]
CD7	Cultivating Overall Quality	Enhances technical and professional teaching while paying attention to developing students’ comprehensive quality.	[14,18,37,74]
CD8	Didactic clarity of teaching	Highlights key points and clarifies students’ difficulties, in developing their skills.	[5,68,72,77]
CD9	Teaching Design	Evaluates the structure and presentation of educational materials created by the teacher to maximize students’ learning experience.	[71,81]
CD10	Mastery of Teaching Content	The teaching content is well-developed, with clear and precise explanations of tasks and problems, highlighting key points and including appropriate examples.	[14,45,71,77]
CD11	Cognitive Skills	Teacher activities aimed at developing students’ critical thinking, creativity, and skills.	[23,75],
CD12	Moderate Depth	The teacher selects and organizes topics to delve into key concepts at a level suitable for the target audience.	[18,77],
CD13	Diverse Approach	The teacher’s ability to present teaching material using various methods and resources to adapt to students’ different learning styles.	[11]
CD14	Content Promotion	Strategies employed by the teacher to highlight the importance and applicability of teaching material, both inside and outside the classroom.	[11]
CD15	Updated Content	Constantly updating content and teaching materials used in instruction, promoting dynamic and engaging learning.	[11,14]
CD16	Clear Content	The teacher’s ability to assess and remove teaching material that is no longer relevant, outdated, or redundant in the curriculum.	[18]
CD17	Data Selection	The teacher’s ability to identify and select educational data and resources that are relevant and valuable to the course’s learning objectives.	[21]
CD18	Modern Educational Materials	The teacher uses current and technological educational materials, promoting dynamic, personalized, and inclusive learning.	[20]
CD19	Course Structure	Evaluates the organization and clarity of course content, including the sequence of topics, learning objectives, materials, and schedule, facilitating effective and organized learning.	[15,78]
CD20	Clear teaching objectives	Refers to the clarity with which the teacher establishes and communicates the learning objectives, guiding the educational process and facilitating the understanding and achievement of clear educational goals.	[14,77]

Teaching effect: Twenty factors were identified, of which the five most studied are teaching effectiveness, positive classroom climate, problem-solving skills, student enthusiasm for learning, and student academic performance (see Table 8).

Table 8. Teaching effect factors for TQUT.

Id	Factor	Description	References
EE1	Positive Classroom Climate	Students are attentive, show great interest in learning, and contribute to a good classroom environment with a harmonious atmosphere between teachers and students.	[14,23,34,67,69,70]
EE2	Teaching Effectiveness	Stimulates students’ interest and learning, enabling them to understand and learn what the teacher teaches.	[5,23,71,74,75,76]
EE3	Problem-Solving Skills	Students exhibit strong problem-solving skills.	[23,42,45,75,77]
EE4	Student Enthusiasm for Learning	The teacher’s ability to connect teaching material with students’ real-life experiences, makes learning more meaningful and applicable.	[34,43,75,78]
EE5	Student Academic Performance	Evaluates the effectiveness of teaching through various methods and the impact of pedagogical strategies employed by the teacher.	[3,34,51]
EE6	Overall Teaching Effectiveness	Assesses the overall effectiveness of the pedagogical methods employed and their suitability for students’ educational needs.	[5,11,72]
EE7	Task Grading	The teacher assigns grades that reflect students’ mastery of course content and provides answers to their questions.	[47,55,79]
EE8	Course Progress	Systematically monitors course performance through regular assessments, projects, and other forms of academic feedback.	[5,30,72]
EE9	Teacher Evaluation Scores	Directly evaluates students’ academic performance in terms of their ability to demonstrate specific knowledge and skills.	[11,27]
EE10	Professional Theoretical Knowledge	Students can better grasp and absorb theoretical knowledge taught in the classroom.	[26,70]
EE11	Formative Assessment	Uses the results of student assessments to reinforce teaching and learning.	[8,13,27]
EE12	Teaching Objectives Assessment	Determines the effectiveness of teaching strategies to ensure students have acquired the expected competencies and knowledge.	[43,74]
EE13	Inspirational Art	The teacher uses creative techniques, visual resources, storytelling, and other pedagogical methods to inspire students.	[18,69,77]
EE14	Teacher Evaluation Skills	The teacher employs diverse forms of assessment to provide a comprehensive measurement of students’ knowledge and skills.	[26,46]
EE15	Student Innovation	Students demonstrate a strong sense of innovation and the ability to develop and apply novel and creative ideas in their learning.	[23,77]
EE16	Teaching Content Organization	Teaching content is accessible, logically sequenced, and comprehensible, facilitating effective learning and information retention.	[43]
EE17	Teaching Structure Design	Well-designed structure that facilitates learning, helping students understand how different concepts are interconnected.	[43]
EE18	Choice of Teaching Methods	The teacher is capable of selecting methods that directly influence the effectiveness with which students acquire knowledge and develop skills.	[14,43,76]
EE19	Educational Effectiveness	The teacher’s ability to positively influence students’ learning and development.	[21]
EE20	Teaching Conditions	The teacher’s ability to provide quality education, including access to pedagogical and technological tools and classroom infrastructure.	[44]

Teacher achievements: 25 factors were identified, of which the 5 most studied are feedback to students, publications and academic contributions, teaching attitude, professional skills, and teacher knowledge level (see Table 9).

Table 9. Teacher achievement factors for TQUT.

Id	Factor	Description	References
LD1	Student Feedback	Teachers actively listen to students’ opinions, provide feedback on their work, and continuously improve teaching and learning practices.	[8,30,34,41,45,47,67,69,73,80]
LD2	Publications and Academic Contributions	Reflects the outcomes of individual and group research and the teacher’s research capacity.	[40,46,49,68,82]
LD3	Professional Skills	Assesses the teacher’s effectiveness in preparing students not only for exams and academic evaluations but also for performance in real professional settings.	[5,18,33,72]
LD4	Teacher Knowledge Level	The teacher’s knowledge of the subject matter is adequate for the courses they teach.	[26,30,40,55]
LD5	Teaching attitude	Teachers influence students with a high sense of responsibility, noble speech and behavior, and a rigorous attitude toward learning.	[40,49,67,77]
LD6	Learning Capacity	Students have the ability for self-directed learning and to understand and master course content.	[14,33,45,77]
LD7	Motivation, Commitment, and Responsibility	The teacher’s ability to foster a sense of responsibility in students, preparing them academically and for future professional and social roles.	[31,48,77]
LD8	Teacher Personality	Evaluates how the teacher’s personal characteristics and interpersonal style influence their teaching and classroom dynamics.	[31,47,76]
LD9	Student Knowledge Level	Measures the students’ understanding and mastery of educational content.	[14,18,77]
LD10	Professional Guidance	The ability to guide and mentor students in their academic and professional paths.	[11,14,76]
LD11	Respect for Students	Teachers respect their students and foster enthusiasm for learning.	[37,67]
LD12	Teacher Training	Teachers’ participation in workshops, seminars, training courses, and other professional development programs to improve their teaching and classroom management skills.	[32,46]
LD13	Teaching Level	The teacher’s ability to develop a curriculum that not only educates but also inspires and prepares students for future academic and professional challenges.	[14,68]
LD14	Integration with Practice and Technology	The teacher uses practical examples, case studies, simulations, specialized software, and other technological tools to enrich teaching.	[30,75]
LD15	Clarity and Emphasis in Lectures	The teacher’s ability to adapt their lecture style to students’ needs and responses.	[30,77]
LD16	Teamwork	Ability to work constructively in interdisciplinary teams.	[38,75]
LD17	Teacher Education	Considers both the initial teacher training (undergraduate and graduate) and their ongoing professional development throughout their career.	[15,37]
LD18	Expanding Student Knowledge	Cultivates a stimulating academic environment that prepares students for continuous and autonomous learning.	[18]
LD19	Academic Level	Evaluate the teacher’s effectiveness in maintaining high teaching standards that promote quality education and prepare students for future success.	[68]
LD20	Highlighting Professional Quality	The educator’s ability to nurture high-level technical talent inspires confidence and respect in students.	[18]
LD21	Teacher Research Level	The teacher’s involvement in academic research activities.	[33]
LD22	Professional Competencies	The skills, knowledge, and attitudes that teachers develop and demonstrate in their educational practice.	[37]
LD23	Teacher’s Subject Affinity	Assesses the teacher’s alignment with the subject based on their education and relevant training.	[38]
LD24	Industry Experience	The teacher’s ability to apply theoretical knowledge in real-world contexts and integrate practical industry experiences into teaching.	[26]
LD25	Professional Literacy	The ability of teachers to keep up to date with the latest trends, skills, and knowledge required in their professional field.	[46]

The number of studies reporting a greater number of factors in each of the categories has been quantified [11,14,15,18,21,23,47,67,68,69,70,71,75,76]. These authors have a minimum contribution of 8 indicators and a maximum contribution of 30 indicators; this approach not only reinforces the validity of the analysis but also facilitates comparisons and allows a more objective interpretation of the results obtained.

4.2. RQ2: What Are the Advances of ML in Predicting Teacher Teaching Quality in Higher Education?

Machine learning techniques are algorithms that are used to predict and discover patterns that are not easily perceived by people, and this approach allows their application in the quality of teaching of the teachers. As indicated by the author [17], machine learning algorithms are computational techniques that can process and analyze large volumes of data to identify patterns and make predictions to evaluate teaching quality objectively. To answer this research question, 30 ML studies are presented that describe the TQUT algorithms used, as shown in Table 10; the most used algorithms are back-propagation neural networks and support vector machines (SVM).

Table 10. Machine learning algorithms for TQUT.

Algorithms	Type of Process	% Accuracy	Reference
BP Neural Network	Prediction	95.00	[18]
	Prediction	94.9	[35]
	Prediction	91.20	[17]
	Prediction	84.50	[14]
	Prediction	----	[36]
	Prediction	----	[49]
	Prediction	----	[19]
Support Vector Machine (SVM)	Classification	99.31	[37]
	Classification	87.60	[80]
	Classification	86.72	[23]
	Classification	78.52	[3]
	Classification	73.00	[75]
Convolutional Neural Network (CNN)	Classification	96.00	[72]
	Classification	91.94	[40]
K-means	Clustering	99.00	[24]
Decision Trees (Extra Trees Regressor)	Regression	98.78	[35]
Deep Neural Network (DNN)	Classification	98.68	[37]
GA-SVM Support Vector Machine	Classification	98.36	[23]
Gradient Boosting Regressor	Regression	97.71	[35]
Random Forest Classifier	Classification	97.35	[35]
CV Logistic Regression	Classification Classification	97.25 61.90	[35] [75]
LGBM Classifier	Classification	97.01	[35]
Multiple Linear Regression	Regression	95.77	[20]
LSTM (Bi-LSTM)	Classification	95.00	[40]
Naïve Bayes (NB)	Classification	91.70	[37]
DAE-SVM	Classification	85.23	[3]
Fuzzy decision tree	Classification	----	[21]
ISSA DRNN	Prediction	95%	[15]
Extreme Learning Machine (ELM)	Classification, Prediction	78%	[75]
BP Neural Network-PSO	Classification	92.3	[76]
Logistic regression DT	Classification	91.2	[13]
SVMs–Logistic regression	Classification	93.8	[13]
DT–SVMs	Classification	92.4	[13]
LSTM RNN–RF	Classification	98.5	[13]
Logistic regression–LSTM RNN	Classification	97.3	[13]
DT–LSTM RNN	Classification	96.9	[13]
BP—Genetic Algorithm (GA)	Prediction	97.8	[14]
CNN-LSTM	Prediction	95.0	[14]
Linear Regression	Regresión	85.3	[80]
Decision Tree Regression	Regresión	82.1	[80]
Random Forest Regression	Regresión	88.9	[80]

4.3. RQ3: What Challenges and Limitations Have Been Identified in Applying ML for Teacher Teaching Quality in Higher Education?

The challenges and limitations in artificial intelligence (AI) are multiple and cover technical, ethical, and social aspects, as described below in Table 11.

Table 11. Challenges and limitations for TQUT.

Studies	Challenges	Limitations
[3,19]	Developing a model that manages complex and nonlinear data.	Requirement for large amounts of high-quality data and significant computational resources.
[17]	Handling complex nonlinear problems and adapting to variations in quality.	Determining index weights and requiring large datasets.
[20]	Designing an adaptable system that combines professional development with quality assurance.	Need for high-quality data and potential for algorithmic bias.
[23]	Selection of evaluation indicators that are scientific, comprehensive, measurable, oriented, and valid.	Linearly inseparable data, the requirement for normalization, and multiple cross-validations.
[21]	Improving the reliability and validity of the data.	The technical complexity of implementing ML in an online evaluation system.
[18]	Integration of theory and practice in evaluation.	Need for large amounts of high-quality data.
[35]	Developing predictive models that handle large volumes of data.	Need to balance datasets and manage high computational complexity.
[36]	Combining scientific methods like AHP with BP to improve accuracy.	Influence of a limited number of experts and the need for continuous testing.
[37]	Selection of relevant features.	Overfitting to ensembles and the need for cross-validation
[40]	Development of evaluation systems that handle heterogeneity and complexity	Obtaining high-quality data, overfitting issues, and algorithmic bias.
[49]	Proper weighting of evaluation indicators.	Requirement for large datasets and computational resources.
[72]	Developing a model that handles large datasets and focuses on representative information.	Need for parameter optimization, resolving occlusion, and handling large displacement.
[76]	Neural network models can be black boxes that are difficult to explain to teachers and administrators.	Using PSO increases computational load and processing time.
[13,14]	A large amount of labeled data is required to properly train models.	Complex models require high processing power and training time.

5. Discussion

The result of this systematic review of the literature on the usefulness of ML in teaching quality processes in higher education shows a growing emphasis on research. This is because ML is a fairly new and emerging field that has only just begun to emerge, so it is likely that this topic will receive greater attention and be a starting point for improving teaching processes in higher education.

5.1. About the Factors

In this study, it was observed that the factors were classified into five dimensions (teaching attitude, teaching method, teaching content, teaching effect, and teacher achievements), where the most studied factor is that of teacher-student interaction, from the point of view where it measures the quality and effectiveness of communications and relationships between the teacher and his students within the learning environment. Two other factors studied of great importance are the one that combines theory with practice, which emphasizes the teacher’s ability to integrate theoretical knowledge with practical applications within the teaching process, and the feedback factor to students that measures the teacher’s ability to respond to each of the students’ concerns or comments and adapt their teaching style and strategies to the diverse needs, learning styles and rhythms of the students.

Some research has considered four and five dimensions of factors, such as the authors Zhong (2023) [69], Ai and Feng (2022) [18], and Wang et al. (2017) [71], that apply to the evaluation of the quality of teachers’ teaching in the areas of physical education, English, among others, each of them with different numbers of variables. The studies of [20], with three dimensions for e-learning for a teaching quality evaluation framework, and Ruan (2023) [21] apply 8 dimensions to ten basic courses of online teaching; they mention that their models can still be improved with new indicators to improve the accuracy of the results. Therefore, it is important to mention that in the evaluation of the quality of teaching performance in universities, scientific, reasonable, valid, and reliable factors should be used. This information is useful in this and future research, which has the relevance and importance of the inventory of factors, with which a TQUT factor framework can be built in higher education.

5.2. About the Algorithms

In this study, a variety of machine learning algorithms used to predict teaching quality in different contexts, scenarios, and approaches were identified, identifying 30 algorithms. Where the three most commonly cited algorithms in the models are BP Neural Network in 6 studies, Support Vector Machine in 4 studies, and Naïve Bayes (NB) in 2 studies, as seen in Table 11. These studies have taken advantage of teaching quality data to propose and apply prediction models, using different techniques and individual and combined algorithms.

The studies of [3,11,17,19,21,23,35,49,70,73] apply a model in the design of a teaching quality evaluation system in the university classroom with different types of algorithms; the authors indicate that it would be interesting to work with a larger number of data sets and add other indicators. Online education is having a great reception worldwide in the study of [21,81]; they apply a model to evaluate the quality of online teaching, but its authors indicate that the model needs additional review for refinement.

Each research analyzed in the review uses different indicators, which makes the process of constructing a framework of factors for the data set a complex process, without leaving aside the different levels of computational power, evaluation criteria in accordance with the environment of the HEIs, and the quality of information to build a conceptual model of TQUT in higher education.

5.3. About Challenges and Limitations

In this study, 14 challenges and limitations were identified, where one of the limitations that the authors indicate is that their studies focus on a limited sample of data. Secondly, in contrast with other ML algorithms, one of the greatest challenges of HEIs is the accumulated amount of data and how it can be used to improve the quality of academic programs.

A significant challenge is the selection of evaluation indicators that are scientific, complete, measurable, targeted, and valid. To ensure that teacher performance is adequately assessed in different aspects, it can be seen that one of the limitations is the evaluation of teachers considered a relatively complex process; therefore, it is necessary to dynamically adjust the indicators and weights of the evaluation system according to the specific subject areas and teacher training requirements. In addition, a limited number of experts can affect the outcome of the evaluation so the evaluation system should be tested and improved.

6. Conclusions

For this study, a systematic review of the literature related to TQUT through ML was conducted. Of the 1347 articles identified, 54 research articles were meticulously selected through analysis, allowing the discovery of advances in the field regarding factors, prediction algorithms, and challenges and limitations. This review identified 111 factors in 54 studies, 30 prediction algorithms in 21 studies, and 14 challenges and limitations in 15 studies. Regarding the factors, we identified five dimensions: teacher attitude (28 factors), teaching method (18 factors), teaching content (20 factors), teaching effect (20 factors), and teacher achievements (25 factors). In particular, the most studied factors were teacher-student interaction, combining theory with practice, and student feedback. In the field of ML advances, neural networks with BP and support vector machines emerged as the most commonly used algorithms.

Most studies focused on teacher performance evaluation but did not measure teacher effectiveness and quality, with limited research on data sets. Regarding algorithms, one of the points to test is unsupervised learning to identify patterns that people do not easily perceive. The challenges of having a high-quality data set to minimize the possibility of algorithmic bias are discussed.

Our research shows that it is important to highlight that universities should apply the use of ML in teaching quality because it allows to analyze data, identifies patterns, and provides useful information, so it can provide a series of significant benefits to improve the quality of decision-making of HEIs, in addition to the fact that it is important to have a system of complete, measurable teaching quality evaluation indicators aimed at improving teacher performance by having a good data set. However, it will be essential to guarantee the ethics and privacy of the data when implementing ML solutions.

The research represents a significant advancement for higher education, particularly in using Machine Learning (ML) technologies to improve teaching quality. Identifying teaching quality factors can provide an innovative framework to address current challenges. This integration of ML can revolutionize higher education, allowing patterns to be identified and predictions to be made to improve data-driven teaching. The findings suggest great potential for future research in using ML in education. Future studies can focus on refining these technologies’ applications and overcoming current challenges and limitations. This area of research can generate new insights into how to apply data analytics to improve educational quality and how to overcome barriers to the effective implementation of these technologies in higher education institutions.

This study had some limitations that must be considered. Only English-language studies were included, and only WoS and Scopus databases were used as data sources. Based on our findings, future research should focus on developing practices and strategies to address TQUT factors to build a factor framework and a system of teaching quality indicators in ML, as well as expanding this study to include other languages and additional databases.