Enhancing Teamwork and Collaboration: A Systematic Review of Algorithm-Supported Pedagogical Methods

Abstract

In today’s interconnected world, teamwork and collaboration are becoming essential competencies across all disciplines. This review examines various pedagogical strategies aimed at nurturing these skills, with a specific focus on integrating algorithms into educational practices. While traditional approaches classify teamwork strategies as either instructor-led or student-led, this review introduces a third method that is based on ML algorithms, which are promising methods for optimizing team composition based on both static and dynamic student characteristics. We investigate the effectiveness of these algorithms in enhancing collaborative learning outcomes compared to conventional grouping methods. In fact, this review synthesizes the findings from 20 key studies on the implementation of these technologies in educational settings, evaluating their impact on learning outcomes, student motivation and overall satisfaction. Our findings suggest that computer-enhanced strategies not only improve the academic and collaborative experience but also pave the way for more personalized and dynamic educational environments. This review aims to provide educators and curriculum developers with comprehensive insights into leveraging advanced computational tools to foster effective teamwork and interdisciplinary collaboration, thereby enhancing the overall quality of education and preparing students for the collaborative demands of the professional world.

Full text

Citation: Wang, X.; Song, G.;
Ghannam, R. Enhancing Teamwork
and Collaboration: A Systematic
Review of Algorithm-Supported
Pedagogical Methods. Educ. Sci. 2024,
14, 675. https://doi.org/10.3390/
educsci14060675
Academic Editor: João Piedade
Received: 12 April 2024
Revised: 6 June 2024
Accepted: 8 June 2024
Published: 20 June 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
education
sciences
Systematic Review
Enhancing Teamwork and Collaboration: A Systematic Review
of Algorithm-Supported Pedagogical Methods
Xunan Wang , Ge Song and Rami Ghannam *
Interactive & Creative Electronics Group, James Watt School of Engineering, University of Glasgow,
Glasgow G12 8QQ, UK; 2694750w@student.gla.ac.uk (X.W.); 2613031s@student.gla.ac.uk (G.S.)
*Correspondence: rami.ghannam@glasgow.ac.uk
Abstract: In today’s interconnected world, teamwork and collaboration are becoming essential
competencies across all disciplines. This review examines various pedagogical strategies aimed
at nurturing these skills, with a specific focus on integrating algorithms into educational practices.
While traditional approaches classify teamwork strategies as either instructor-led or student-led, this
review introduces a third method that is based on ML algorithms, which are promising methods for
optimizing team composition based on both static and dynamic student characteristics. We investigate
the effectiveness of these algorithms in enhancing collaborative learning outcomes compared to
conventional grouping methods. In fact, this review synthesizes the findings from 20 key studies on
the implementation of these technologies in educational settings, evaluating their impact on learning
outcomes, student motivation and overall satisfaction. Our findings suggest that computer-enhanced
strategies not only improve the academic and collaborative experience but also pave the way for
more personalized and dynamic educational environments. This review aims to provide educators
and curriculum developers with comprehensive insights into leveraging advanced computational
tools to foster effective teamwork and interdisciplinary collaboration, thereby enhancing the overall
quality of education and preparing students for the collaborative demands of the professional world.
Keywords: teamwork; algorithm; collaboration; pedagogy; learning
1. Introduction
Effective problem solving and innovation across different disciplines no longer rely
on individual expertise [
1
]. Collaboration in teams has become increasingly crucial [
2
].
Today’s global challenges demand a mixture of perspectives, knowledge and skills that
surpass the capacity of any single individual. Therefore, teamwork and collaboration are
essential competencies across all fields of study and professional practice [3].
Various pedagogical approaches have been explored to encourage teamwork and
collaboration, which can be categorized as either instructor-led or student-led. Instructor-
led approaches leverage the instructor’s expertise in creating teams and scenarios that
simulate real-world collaborative challenges [
4
,
5
], whereas student-led approaches rely on
students organizing and managing their teams [6].
Collaborative learning, defined as learning within a group through discussion and
joint knowledge construction [
7
], has been effectively employed in education as a pedagogic
strategy to enhance and complement individual learning [
8
] as well as boost academic
performance [
9
]. Compared with traditional grouping methods such as random and self-
organized grouping, the advanced algorithm-supported grouping approaches not only
improve academic performance but also provide a better collaborative experience, enhance
learning motivation and increase overall satisfaction [10,11].
Effective automatic grouping methods rely on three crucial aspects. First, selection
criteria determine who gets placed together. These criteria could be skills, interests, learning
styles, or previous experience. Second, the grouping type influences how people are
Educ. Sci. 2024,14, 675. https://doi.org/10.3390/educsci14060675 https://www.mdpi.com/journal/education

Educ. Sci. 2024,14, 675 2 of 24
assigned. Are they meant to collaborate, share knowledge, or simply interact socially? The
purpose of the group dictates this. Finally, grouping algorithms are the computer programs
that take these criteria and type into account, automatically assigning people to groups
based on the chosen parameters [11].
According to Moreno et al. [
7
], effective grouping promotes better interaction and
produces better learning outcomes by taking into account a variety of group formation
characteristics. Academic achievement is not the only requirement for a successful group.
It has been shown that a variety of learner-related factors, such as learning styles, commu-
nication and leadership abilities, personality traits, social interaction and knowledge level
significantly affect the grouping process [12–15].
Li et al. [11]
emphasize the importance of distinguishing between static and dynamic
student characteristics. They defined static characteristics as the fixed attributes of students,
such as learning styles, communication and leadership skills, which remain unchanged
throughout the learning process. In contrast, dynamic characteristics are those that cannot
be determined at the beginning of the course and change throughout the learning process,
such as levels of social interaction.
Once the grouping criteria are established, regardless of the specific characteristics,
three types of groupings can be formed based on the degree of uniformity or similarity:
homogeneous, heterogeneous and mixed grouping [
16
]. In the homogeneous grouping,
each group is formed with individuals who share greater similarities. In the heteroge-
neous grouping, each group is characterized by greater diversity among its members.
Mixed grouping involves searching for homogeneity in certain characteristics while seek-
ing heterogeneity in others, resulting in groups that exhibit both significant similarities and
differences among all individuals. Research has shown that in an inquiry-based learning
context, homogenous grouping is more beneficial for teaching than heterogeneous group-
ing [
17
]. However, in a didactic setting, heterogeneous grouping is more effective [
18
].
According to the majority of studies, heterogeneous grouping is the practice of instructing
students in the same classroom—between two and five students—with various ages and
skill levels together. As stated by Sukstrienwong [
12
], students of advanced, average
and low ability have benefited academically and socially from heterogeneous grouping in
elementary grade levels.
Several algorithmic approaches have been presented by the learner group formation
research community to effectively address this difficulty. Algorithms present a promising
new method for enhancing team formation. Examples of these methods include clustering,
the greedy algorithm, the ant colony algorithm and genetic algorithms [
19
–
22
]. Genetic
algorithms (GAs) and other evolutionary algorithms are strong solutions that use machine
learning to calculate several parameters. These algorithms can analyze large amounts of
data and identify patterns for effective team formation. For example, evolutionary machine
learning matches the multidimensional input of student model traits and is adaptable to
various group compositions to deliver a genetic solution for created groups [
7
,
12
]. The
k-means algorithm is a data mining technique used for group formation by identifying
patterns and extracting information from big data sets. It uses methods that combine
machine learning, statistics and database systems [19].
Numerous studies have addressed the challenge of group formation in collaborative
learning by creating systems designed to create groups based on their proposed approaches.
As shown in Figure 1, Liang et al. [
23
] created an intelligent group formation system
that incorporated a variety of algorithms, including homogeneous and heterogeneous
algorithms, as well as the jigsaw algorithm. This system offered a comprehensive solution
for supporting teachers in the formation and analysis of groups using learning log data
derived from the BookRoll learning system [
24
]. Moreover, Sukstrienwong [
12
] proposed a
GA with a particular fitness function that focuses on using students’ learning styles and
educational backgrounds as the core criteria for group assignments. This approach aimed to
establish a fair grouping mechanism, ensuring optimal allocation of heterogeneous students
within groups to maximize learning outcomes. To achieve this, researchers developed a

Educ. Sci. 2024,14, 675 3 of 24
web-based application called “Genetic Algorithm for Student Group Formation (GAFSG)”.
According to the authors, GAFSG supported various group sizes and diverse grouping
attributes. It allowed teachers to adjust and determine various attributes to be considered
during the student grouping process and consider multiple heterogeneity factors based on
the specific needs of the student population.
Figure 1. Screenshots showcasing examples of different team formation web applications from the
literature. (a). Result page of the system based on learning log data from BookRoll learning system
(reproduced from [
23
] under a CC BY 4.0 license). (b) This system uses a novel Genetic Algorithm
for group recommendations (reproduced from [
25
] under a CC BY 4.0 license). (c) Standalone
web application that uses artificial intelligence techniques such as coalition structure generation,
Bayesian learning, and Belbin’s role theory to create effective teams. Reprinted from [
26
], Copyright
(2016), with permission from Elsevier. (d) Web-based application called GAFSG for team formation.
Reproduced from [12] under a CC BY license.
However, among those applications presented in Figure 1, two studies developed spe-
cific applications and conducted experiments to provide innovative technical contributions,
with plans for future improvements and performance enhancements [
23
,
25
]. One study
developed a standalone web application integrated into Sakai, which is the e-learning plat-
form of the Universitat Politècnica de València [
26
]. Only one study explicitly mentioned a
publicly accessible web application [
12
], but searches using the provided URL and name
yielded no results. Researchers still need to improve the accessibility and usability of their
applications to better promote research findings and serve in educational practices.
In this field, some highly cited review studies have conducted in-depth analyses and
summaries of computational techniques (i.e., algorithms) and students’ key attributes used
to support team formation in collaborative learning environments [
16
,
27
]. Furthermore,
another review study comprehensively explored the application of GA in team building,
covering aspects such as student attributes, genetic operators, initial settings, and termina-
tion conditions [
22
]. While existing research has contributed to exploring the application of
algorithms in team formation, they may have overlooked the comprehensive evaluation of

Educ. Sci. 2024,14, 675 4 of 24
these algorithms’ effects in actual teaching environments. Therefore, this systematic review
examines the various algorithms’ applications in team formation and the types and criteria
of groups. Additionally, it compares the learning effects of algorithm-supported teaming
with traditional teaming methods in actual teaching environments and reviews assessment
methods and experimental design (data collection and analysis methods). Additionally, this
paper also outlines the student academic levels and professional backgrounds involved in
algorithm-supported teaming. Overall, this review may also serve as an update to previous
literature reviews in this field. This comprehensive analysis aims to provide educators
and researchers with deeper insights to help them effectively use algorithms to promote
collaborative learning and interdisciplinary collaboration.
2. Methodology
2.1. Research Questions
This systematic review explores the intersection of algorithms and pedagogical meth-
ods aimed at enhancing teamwork and collaboration. To achieve this objective, specific
research questions were formulated, and the pertinent literature was curated to address
them. The research questions (RQs) were as follows:
RQ1: What algorithms are used for team formation?
RQ2: What grouping types and criteria are used for team formation?
RQ3: What are the results of group formation using algorithms? How are the results
evaluated?
RQ4: What educational content is involved in automatic team formation?
2.2. Review Protocol
To organize this systematic review, we initiated a review protocol. In this section, the
search strategy, search execution, inclusion and exclusion criteria and screening procedure
for choosing pertinent research papers are all presented.
2.2.1. Search Strategy
There were two steps in the search strategy. Initially, we formulated the search string
based on our topic and keywords. Subsequently, we chose appropriate electronic databases
for the search execution.
In the first step, three main keywords were defined: “team formation”, “algorithm”
and “collaborative learning”. Team formation refers to the process of assembling individ-
uals into groups to work collaboratively toward a common goal. An algorithm is a set
of step-by-step procedures or instructions designed to perform a specific task or solve a
particular problem. Table 1shows the keywords, definitions and their respective synonyms.
Table 1. Keywords, definitions and synonyms used for shortlisting articles.
Keyword Definition Synonyms
Team Formation [27]
Team formation refers to the process of
assembling a group of individuals into a
team based on their skills, experiences,
preferences and other relevant
characteristics to achieve a specific task or
project goal.
Team creation, team
design, group
formation, group
creation, group
design
Algorithm [27]
An algorithm is a finite set of instructions
carried out in a specific order to perform a
particular task.
Software, technique,
approaches
Collaborative
Learning [27]
Collaborative learning is the educational
approach of using groups to enhance
learning through working together.
Cooperative learning,
group learning, team
learning

Educ. Sci. 2024,14, 675 5 of 24
The search string was formulated using the keywords “team formation”, “algorithm”
and “collaborative learning”, which were contextually relevant and included terms con-
nected to the research question, along with their synonyms. We defined the Boolean logic
search string as follows: (“Team formation” OR “Team creation” OR “Team design” OR
“Group formation” OR “Group creation” OR “Group design” in the abstract) AND (Algo-
rithms OR Software OR Technique OR Approaches in the abstract) AND (“Collaborative
learning” OR “Cooperative learning” OR “Group learning” OR “Team learning” in the
abstract). Aside from this, to ensure both quality and accuracy, only journal articles from
the last 10 years were screened.
In the second step, several critical databases related to the fields of computer science
and engineering were applied to find journal papers relevant to our research. To choose the
appropriate digital libraries, we followed the suggestions of the library lists recommended
by Jaziar [
28
] and Dyba [
29
]. Then, we combined and shrimped the libraries to cover
the most relevant field of educational technology. The list of those electronic databases
included Web of Science, IEEE Xplore Digital Library, ProQuest, ACM Digital Library and
SpringerLink.
2.2.2. Search Execution
Each chosen database’s search engine operates using distinct protocols and criteria.
Consequently, we slightly modified the search phrase created in this study to suit each
database for our search. Table 2displays the details of the search conducted. The first
column indicates the 5 databases used, the second details the search string and filters
implemented for each digital library, and the third lists the original search outcomes in a
total of 7724 papers. These results encompass peer-reviewed scientific articles in English
published from 2014 to 2023.
Table 2. Search conduction and original results in each database.
Digital Library Search String Original Results
Web of Science
AB = Team formation OR Team creation OR Team design OR Group
formation OR Group creation OR Group design AND
AB = Algorithms OR Software OR Technique OR Approaches AND
AB = Collaborative Learning OR Cooperative Learning OR Group
Learning OR Team Learning. Filter used: document type = article;
language = English; publication years = 2014–2023.
5646
IEEE Xplore Digital Library
(“Abstract”: Team formation OR “Abstract”: Team creation OR
“Abstract”: Team design OR “Abstract”: Group formation OR
“Abstract”: Group creation OR “Abstract”: Group design) AND
(“Abstract”: Algorithms OR “Abstract”: Software OR “Abstract”:
Technique OR “Abstract”: Approaches ) AND (“Abstract”:
Collaborative Learning OR “Abstract”: Cooperative Learning OR
“Abstract”: Group Learning OR “Abstract”: Team Learning). Filter
used: document type = journals; publication years = 2014–2023
311
ProQuest
Abstract(“Team formation” OR “Team creation” OR “Team design”
OR “Group formation” OR “Group creation” OR “Group design”)
AND Abstract(Algorithms OR Software OR Technique OR
Approaches) AND Abstract(Collaborative Learning OR Cooperative
Learning OR Group Learning OR Team Learning). Filter used:
document type = journals; language = English; publication
years = 2014–2023)
59

Educ. Sci. 2024,14, 675 6 of 24
Table 2. Cont.
Digital Library Search String Original Results
ACM Digital Library
((Abstract: “team formation”) OR (Abstract: “team creation”) OR
(Abstract: “team design”) OR (Abstract: “group formation”) OR
(Abstract: “group creation”) OR (Abstract: “group design”)) AND
((Abstract: algorithms) OR (Abstract: Software) OR (Abstract:
technique) OR (Abstract: approaches)) AND ((Abstract: collaborative
learning) OR (Abstract: cooperative learning) OR (Abstract: group
learning) OR (Abstract: team learning)) Applied filters: document
type = journals; publication years = 2014–2023
11
SpringerLink
(“Team formation” OR “Group design” OR “team design” OR
“group formation” OR “group creation” OR “team creation”) AND
(Algorithms OR software OR technique OR approaches) AND
(Collaborative learning OR cooperative learning OR group learning
OR team learning). Applied filters: document type = journals;
language = English; publication years = 2014–2023
1697
2.2.3. Inclusion and Exclusion
In the next step, we established a set of inclusion and exclusion criteria for this study.
This was to initially screen the preliminary results of the collected papers and determine
whether they met the analysis conditions.
The inclusion criteria were as follows:
• Journal articles with peer-reviewed publishing between 2014 and 2023;
• Applications in the field of education in face-to-face courses;
• Experimentation with students as participants.
The exclusion criteria were as follows:
• Conference papers, books, book chapters and pre-print articles;
•
Applications not in the field of education or not in face-to-face courses (business,
company, sports, hospital, robotics, online learning or digital learning);
• Experimentation with agents.
2.2.4. Screening of Papers
Figure 2provides a detailed depiction of the comprehensive process undertaken for the
literature search results. This procedure commenced with an initial collection of research
and, after a series of screenings, yielded articles suitable for in-depth analysis. Within five
pre-selected databases, we employed Boolean queries for advanced searching, leading to
the discovery of 7724 articles. From this, 1476 duplicate articles found across various digital
libraries were eliminated, ensuring each article was represented only once. Subsequently, in
adhering to the predetermined inclusion and exclusion criteria, we screened the remaining
6248 articles. Initially, based solely on their titles, we excluded 5583 articles related to
business, companies, employees, buildings, hospitals and sports such as football, retaining
only those pertinent to the field of education. Building on this, we reviewed the abstracts
of 665 articles and dismissed those focusing on online education, digital education and
distance learning, as our primary interest centered on offline learning strategies for student
team formation. Concurrently, studies related to robotics and intelligent agents were also
excluded. This step resulted in the elimination of 582 articles. Finally, we undertook
a full-text review of the remaining 83 articles, further excluding 63 and culminating in
the selection of 20 journal articles that formed the foundation for our research analysis.
While we acknowledge the value of conference and pre-print articles in reporting the latest
research, we focused exclusively on peer-reviewed journal articles to ensure that only
reliable and empirically validated results were considered.

Educ. Sci. 2024,14, 675 7 of 24
Figure 2. PRISMA flowchart for systematically reviewing the literature. Twenty articles met our
inclusion and exclusion criteria.
2.3. Coding Scheme
To elucidate the contributions of each paper more effectively, we adopted and devel-
oped five categorization techniques based on the research questions of this study. The
coding schemes were as follows:
(1)
Bibliometric Analysis: Drawing on the research by Zou et al. [
30
], bibliometric infor-
mation can be categorized by publication year, issuing journal, associated disciplines
and rankings.
(2)
Research Design: Luo [
31
] further classified research design into six dimensions:
research type, research methodology, the number of experiments, study duration,
data collection methods and data analysis techniques.
(3)
Criteria for Team Formation: We delineated the criteria for team categorization into
four main categories: personality, homogeneity versus heterogeneity, social networks
and skills.

Educ. Sci. 2024,14, 675 8 of 24
(4)
Algorithmic or Technological Approaches: Based on the research by Maqtary and
Bechkoum [
16
], team formations supported by algorithms encompass genetic algo-
rithms, clustering algorithms, k-means algorithms, and other technological methods.
(5) Evaluation Methods: We classified the methods of assessing student team performance
into questionnaires, interviews and academic grades.
3. Results
We conducted a systematic literature review to explore the development and appli-
cation of group formation algorithms in collaborative learning environments. A total of
20 articles satisfied our search criteria. As shown in Figure 3, there’s been a clear rise in
publications on team formation algorithms in collaborative learning since 2020. This trend
peaked in 2021, with the highest number of journal articles published. In fact, this rise seems
to coincide with the COVID-19 pandemic that began in early 2020. As remote learning
and digital collaboration tools became essential, researchers may have directed their focus
towards developing algorithms for enhancing online and hybrid learning environments.
The peak in publications in 2021 could reflect these efforts reaching maturity, whereas the
decline in 2022 might suggest a stabilization of research trends as researchers adapt to the
‘new normal’ of education.
Nevertheless, while there was a decrease in 2022, the overall trend suggests ongoing
research activity and strong academic interest in this field. This not only reflects continued
focus on collaborative learning and group formation algorithms but also highlights their
potential and importance for future research.
Figure 3. Number of publications during the past 10 years on team formation, with peak interest
in 2021.
3.1. Algorithms of Team Formation
In this section, we provide a comprehensive and detailed analysis of the algorithms
used in 20 articles published between 2014 and 2023. Forming groups is a classic optimiza-
tion challenge that demands an appropriate solution from among tens of thousands of
possible group configurations. The algorithm is one of the critical factors used to automat-
ically generate teams. Table 3illustrates the utilization of algorithms throughout all the
literature under analysis. This study discovered that GAs were the most favored approach
for grouping within the academic community, with research utilizing GAs constituting 70%
of the total shown in Figure 4. GAs are capable of obtaining the optimal solution from a
multitude of possible solutions within a limited time frame. Their advantage lies in the
ability to flexibly generate groups based on different criteria while maintaining a certain
level of computational efficiency [
12
,
15
,
32
]. The flexibility and efficiency of this algorithm
may be one of the key reasons for its widespread application across various fields and
problem-solving strategies.

Educ. Sci. 2024,14, 675 9 of 24
Figure 4. The vast majority of articles (70%) used GAs for team formation.
A GA simulates the principles of natural selection and genetics, optimizing solutions
through an iterative process and making them particularly effective in tackling complex
issues, especially when multiple factors and constraints need to be considered. A GA’s
general design is shown in Figure 5. It starts with generation 0 (Gen = 0) and a randomly
selected population for the first population. All individuals are evaluated by a fitness
function. Reproduction, crossover and mutation are three common GA operators that take
place throughout a single generation to produce new offspring. These operators aim to
preserve the chromosomes (or part of them) that represent superior solutions under the
principles of natural selection [
33
]. In general, the reproduction operator is more likely
to use a fitness function to choose individuals who will make up the algorithm’s next
generation. Two chromosomes (parents) are combined by the crossover operator to create
new chromosomes (offspring). This can be applied in GA in a variety of ways, including a
heuristic crossover, two-point crossover and single-point crossover [
34
]. Therefore, GAs
are not only valued in theoretical research but also demonstrate significant potential and
value in practical applications.
The following content elaborates on the innovative improvements made to GAs in
various aspects in numerous studies. These enhancements include the optimization and
adjustment of genetic operators, as seen in [
10
,
14
,
25
,
32
,
35
], aiming to enhance the search
efficiency and quality of solutions of the algorithm. Additionally, some studies focused on
refining the fitness functions [
12
,
15
], aiming to more accurately assess individual fitness
and promote a more effective evolutionary direction. Moreover, improvements to the
algorithmic model itself have been a focal point [
11
], adjusting the algorithm’s structure
and processes to accommodate more complex application scenarios. Furthermore, several
studies have explored the integration of GAs with other algorithms [
23
,
36
,
37
], a cross-
disciplinary fusion that not only expands the application scope of GAs but also provides
new insights and methods for tackling more complex problems. These advancements and
integrations continuously propel GAs toward greater efficiency, precision and adaptability,
showcasing the immense potential and value of GAs in solving real-world problems.

Educ. Sci. 2024,14, 675 10 of 24
Figure 5. A Genetic Algorithm (GA) general design flowchart and process [12].
For instance, Chen and Kuo [
15
] provided an innovative approach to forming groups
using GAs that incorporates a penalty function. This method takes into account the
heterogeneity of students’ knowledge levels and learning roles, as well as the homogeneity
of social interactions among group members, as assessed through social network analysis.
A fitness function was employed to assess the availability, and subsequently, a globally
optimized solution was developed by allocating varying weights to student characteristics.
As a result, collaborative groups with balanced educational attributes are formed within
a problem-based collaborative learning context. Additionally, in Krouska and Virvou’s
study [
32
], the authors introduced a novel GA for grouping students within a social
network-based learning system. This approach allows for a more thorough exploration of
the problem space and introduces new genetic information into the population, effectively
preventing the algorithm from getting stuck in local optima. Aside from that, this approach
outperforms the basic GA technique in terms of efficiency and accounts for a broader range
of parameters than typically observed.
However, beyond the enhanced GA, several studies have explored the integration
of GAs with other computational methods. This interdisciplinary approach leverages the
strengths of different algorithms to address complex problems more effectively. For exam-
ple, in Berge and Mark’s study [
37
], they introduced the “Team Machine” as a tool designed
to form student teams with the highest level of diversity. The Team Machine incorporates a
variety of search algorithms, including the Greedy Randomized Adaptive Search Procedure
(GRASP) and GAs. The GRASP, serving as a precise local search technique, is focused on
finding optimized solutions within a constrained search area. On the other hand, GAs, as
a population-based search strategy, excel in expanding the search horizon by exploring a
wider array of solution spaces, thereby enhancing the quality of solutions initially identified

Educ. Sci. 2024,14, 675 11 of 24
by the GRASP. By synergistically utilizing both algorithms, this method not only bolsters
the comprehensive exploration of potential team configurations but also aims to refine and
elevate the best solutions discovered.
Table 3. Overview of technical team formation algorithms.
Algorithms Number Literature
Genetic algorithm 14 [10–15,23,25,32,35–39]
Team formation algorithm based on coalition structure
generation Belbin’s theory and Bayesian learning 1 [26]
Variable neighborhood search algorithm 1 [40]
k-means algorithm 1 [19]
Group algorithm 1 [41]
Cluster and prune 1 [42]
Minimum entropy collaborative grouping 1 [43]
Furthermore, as demonstrated in Figure 4and Table 3, among the selected articles
for this analysis, six papers employed non-genetic algorithms. Each of these algorithms
was unique, having been independently developed and applied, including the variable
neighborhood search algorithm [
40
], the k-means algorithm [
19
], the group algorithm [
41
],
the cluster and prune method [
42
] and minimum entropy collaborative grouping [
43
].
The diverse selection of algorithms in these studies not only enriches the methodological
landscape of group formation but also offers a broader perspective and potential solutions
for addressing specific grouping challenges. For instance, Lambi´c et al. [
40
] developed
an application utilizing the variable neighborhood search algorithm, aimed at addressing
the process of group formation as a mathematical optimization problem. Aside from that,
Ramos et al. [
19
] employed the k-means algorithm, integrating three distinct similarity
distance metrics—the Euclidean distance, Manhattan distance and cosine similarity—along
with key student attributes extracted from learning paths for group formation. This com-
prehensive approach of applying multiple metrics and student characteristics for grouping
demonstrates an innovative strategy and method for addressing complex grouping chal-
lenges in the educational sector.
In summary, this trend highlights the widespread application and dominant position
of GAs in the research of automatic group formation, motivating researchers and developers
to further explore GAs and other optimization techniques. Despite the clear advantages of
GA, 30% of the studies employed other algorithms, indicating that there is still room for
exploring new methods and improving existing ones in the field of automatic grouping.
The diversity of these methods not only provides researchers with the flexibility to choose
or develop the most suitable algorithm based on specific needs but also paves the way
for interdisciplinary collaboration. It encourages experts from fields such as computer
science, artificial intelligence, educational technology and psychology to work together in
developing more efficient and intelligent grouping systems.
3.2. Grouping Types of Team Formation
In the automated process of forming groups, the selection of algorithms, along with
the type of grouping and the characteristics of the students chosen, play a pivotal role
in determining the effectiveness of the grouping. Group formations can be categorized
into three main types: homogeneous, heterogeneous and mixed. This section provides a
focused review of the types of group formations achievable through algorithms, specifically
utilizing the algorithms mentioned earlier (such as GAs) and the required characteristic
attributes to form groups with homogeneous or heterogeneous features according to op-
timization functions. As shown in Figure 6, 15% of the studies adopted homogeneous
grouping methods [
14
,
19
,
35
], while 30% of the research opted for heterogeneous group-

Educ. Sci. 2024,14, 675 12 of 24
ing approaches [
11
,
12
,
26
,
32
,
42
,
43
]. Most notably, over half of the studies explored mixed
grouping methods that combined both homogeneous and heterogeneous characteristics,
highlighting the significance and prevalence of mixed grouping methods in current research.
Figure 6. In educational and organizational contexts, team or group formation can be categorized
into two primary types based on the characteristics of the members: heterogeneous grouping and
homogeneous grouping. Most of the shortlisted articles adopted a mixed grouping method.
3.2.1. Homogeneity
Homogeneous grouping gathers members with similar characteristics, creating groups
that exhibit a high level of similarity among members when all characteristics are con-
sidered collectively. According to Jensen and Lawson [
17
], when students with similar
beginning reasoning abilities are grouped together, they tend to have more positive at-
titudes toward collaboration in the context of inquiry-based learning, and this leads to
greater performance. Similarly, in their study, Sanz-Martínez et al. [
44
] discovered that
when learning engagement is homogeneous, team assignments exhibit higher quality due
to increased interactions and self-efficacy. While Vygotsky [
45
] suggests that heterogeneity
among group members and their resources is beneficial, homogeneous compositions in
learning engagement patterns can prevent the neglect and isolation of learners during
group activity [46].
In this systematic review, only three studies exclusively utilized homogeneous group-
ing methods [
14
,
19
,
35
], accounting for 15% of the total. For instance, Oscar et al. [
35
]
introduced a strategy for forming homogeneous groups within collaborative learning
settings aimed at enhancing cooperation and improving educational outcomes, both col-
lectively and individually. The establishment of these groups relies on personality traits,
specifically employing the Big-Five personality model (extraversion, agreeableness, consci-
entiousness, neuroticism and openness). Furthermore, they measured the homogeneity
across all students in a group using the fitness function of a GA. Empirical evidence has
shown that homogeneous groups formed through GAs achieve better learning outcomes
compared with traditional methods of grouping students based on their preferences. Simi-
larly, Ramos et al. [
19
] demonstrated through experimental validation that 75% of students
experienced an improvement in their grades when grouped homogeneously.
3.2.2. Heterogeneity
Heterogeneous grouping, in contrast, combines members with differing or complemen-
tary characteristics. This arrangement results in greater diversity among group members,

Educ. Sci. 2024,14, 675 13 of 24
fostering a culture of mutual complementarity and learning that can boost the team’s
capacity for innovation and problem-solving diversity. In the didactic condition, Jensen
and Lawson [
17
] found that heterogeneous groups performed better than homogenous
groups. Likewise, in terms of knowledge, skills and abilities, including team behavior skills,
heterogeneous groups are likely to be more successful than teams that are homogeneous.
This is because diverse teams have access to a wider range of knowledge and perspectives,
and members can learn from each other and generate new ideas by combining their qual-
ifications [
47
,
48
]. In conclusion, the practice of heterogeneous grouping is increasingly
favored as it more effectively accommodates a variety of educational settings, leading to
the desired outcomes in collaborative learning.
As illustrated in Figure 6, six studies opted for heterogeneous grouping criteria [
11
,
12
,
26
,
32
,
42
,
43
], which accounted for 30% of the total. Based on Belbin’s team role theory,
Alberola et al. [
26
] developed an artificial intelligence tool for creating heterogeneous teams
in the classroom. This theory suggests that a successful team should consist of eight distinct
roles—plant, resource investigator, coordinator, shaper, monitor evaluator, team worker,
implementer and finisher—to foster successful teamwork. Compared with traditional team
formation methods, students believe this new approach enhances the level of collabora-
tion, leading to greater satisfaction with their teammates’ cooperation and a higher regard
for their teammates. Krouska and Virvou [
32
] developed an innovative GA for creating
heterogeneous groups. The algorithm takes into account the three key dimensions of learn-
ing within the social networking-based learning environment—academic, cognitive and
social dimensions—while utilizing the characteristics generated from these dimensions to
foster team collaboration and enhance learning outcomes at both the group and individ-
ual levels. The vast majority of students reported receiving effective support from other
group members during project work, indicating that the heterogeneous group configuration
achieved the desired positive effects in practice. To create heterogeneous groups more
efficiently, Toni and Ramon [
43
] designed an advanced algorithm called “minimum entropy
collaborative grouping” based on complex network theory. The results indicate that groups
formed through this algorithm are more efficient, exhibit lower uncertainty, have stronger
interconnections and demonstrate a higher level of maturity.
3.2.3. Homogeneity and Heterogeneity
Mixed grouping adopts a more adaptable strategy, applying homogeneous grouping
for certain characteristics while utilizing heterogeneous grouping for others. This method
not only preserves similarities among group members but also introduces diversity within
the group, promoting mutual understanding and sparking innovation and adaptability
within the team.
In 55% of the studies, the focus was not solely on homogeneous or heterogeneous
grouping criteria. As shown in Table 4, among these 11 articles, three studies adopted
criteria for intergroup homogeneity and intragroup heterogeneity [
13
,
36
,
37
], two im-
plemented mixed grouping strategies that combine homogeneous and heterogeneous
characteristics [
15
,
40
], another two allowed for either homogeneous or heterogeneous
grouping [23,39],
and four papers offered the flexibility to choose among homogeneous,
heterogeneous or mixed grouping methods [
23
,
25
,
38
,
41
]. This demonstrates the diversity
and flexibility of grouping approaches.
Lin et al. [36] created an advanced method that combines GAs and the Technique for
Order Preference by Similarity to Ideal Solution method. Based on this, a web-based group-
ing support system was developed, aimed at assisting educators in effectively grouping
students according to intergroup homogeneity and intragroup heterogeneity. In this way,
students with stronger skills within a group can support those who need help. That aside,
groups are balanced based on multiple criteria, which ensures equity among groups and
reduces disparities. As a result, this method not only makes competition between groups
fairer but also enhances learning enthusiasm and helps improve learning outcomes. In
addition, Chen and Kuo [
15
] implemented a novel method for creating groups that combine

Educ. Sci. 2024,14, 675 14 of 24
GAs with penalty functions. The goal was to construct collaborative learning groups that
had a balanced distribution of learning qualities. This approach considers the range of
students’ knowledge levels and learning roles, as well as the regularity of social interactions
among group members. This strategy promotes the formation of collaborative groups
that have a balanced mix of learning abilities. It not only improves students’ academic
achievement but also enriches their interactions in a problem-solving-focused collaborative
learning context.
Table 4. Integrated homogeneous and heterogeneous grouping algorithm applications.
Group Type Number Research
Inter-group homogeneity AND intra-group heterogeneity
3 [13,36,37]
Homogeneity AND heterogeneity 2 [15,40]
Homogeneity OR heterogeneity 2 [23,39]
Homogeneity OR heterogeneity OR mixed 4 [10,25,38,41]
The mixed grouping approach provides researchers with the greatest freedom, en-
abling them to select the most suitable grouping strategy according to their specific educa-
tional goals and learning tasks. This strategy integrates the benefits of both homogeneous
and heterogeneous grouping, fostering mutual comprehension and cooperation among
members while also encouraging team creativity and flexibility. Educators and researchers
are exploring various ways for mixed grouping to address the varied learning demands and
objectives in a more personalized and dynamic manner. According to a detailed analysis of
20 pieces of research, it is clear that both heterogeneous and homogeneous grouping have
their own advantages and specific situations where they are suitable. However, the mixed
grouping approach garnered more attention due to its flexibility and multiple benefits. The
mixed grouping method’s diversity and adaptability may significantly improve learning
results, team cooperation and innovative capacities. This approach enables educators to
adapt grouping strategies flexibly according to the particular requirements and learning
objectives of students, thus enhancing support for the learning process.
3.2.4. Characteristics
There are two types of student characteristics: dynamic and static [
11
]. Static qualities
include things like gender, age, past knowledge and learning styles, which essentially never
change or at least do not change rather quickly. On the other hand, dynamic traits, like a
student’s interaction levels and emotional states, tend to change over the course of their
learning process since they are difficult to fully capture at one moment in time. In this
literature review, as shown in Figure 7, when it came to research on employing student
characteristics for grouping, the most common approach was the use of static features,
which were referenced in 11 papers (or 55% of the total). Only 25% of the studies took
into account both static and dynamic properties. This type of research was less common.
Studies based solely on dynamic characteristics for grouping were quite rare, with only
one paper addressing this approach. Additionally, three papers described teachers defining
grouping criteria based on the content of collaborative activities, without specifying the
exact characteristics to be included.
One possible reason for the widespread use of static characteristics in grouping re-
search is that these traits cover several key dimensions needed for grouping in the edu-
cational field. Especially in educational settings, a student’s prior academic performance,
study habits and professional skills as well as communication and leadership skills, which
are required for teamwork, are crucial. These static characteristics include learning styles,
prior academic performance, learning roles, gender, age, major, coding skills, leadership
level, knowledge levels, communication skill level and personality traits. Due to their
relative stability and ease of measurement, these traits have become the preferred choice

Educ. Sci. 2024,14, 675 15 of 24
in educational grouping research. In contrast, dynamic characteristics mainly involve
students’ social interactions and emotional states, which may change during the learning
process, including social interactions, interpersonal relationships and social networks. Due
to the fluidity and complexity of dynamic characteristics, studies based solely on these
traits for grouping are relatively rare. Most research that included dynamic characteristics
tended to also incorporate static traits, aiming to achieve a more comprehensive student
profile and more effective grouping strategies through integrated analysis. Integrating
both static and dynamic student characteristics results in a more effective collaborative
outcome compared with relying solely on one type of characteristic [
11
,
32
]. For example, to
create optimal groups, Chen and Kuo [
15
] considered the topic knowledge levels, learning
roles and social interactions of the students. Similarly, Krouska and Virvou [
32
] examined
17 variables related to social, cognitive and academic domains to create groups.
Figure 7. “Static” and “dynamic” group formation refers to how group memberships are determined
and whether they can change over time. These approaches can significantly influence the dynamics
and outcomes of team collaboration. Most research articles use “static” grouping, whereby group
composition is fixed at the beginning of the project or learning period and remains unchanged
throughout the duration of the activity.
In conclusion, static characteristics are frequently used due to their broad coverage of
key dimensions required for educational grouping and their relative stability and ease of
measurement. Although dynamic characteristics are crucial for understanding students’
social and emotional states, they often need to be combined with static traits in practical
applications to achieve more effective student grouping. This finding points to potential fu-
ture research directions, namely exploring how to better utilize dynamic characteristics and
how to optimize the combination of dynamic and static traits to achieve higher efficiency
and effectiveness in educational grouping.
3.3. Outcomes and Evaluation
3.3.1. Reviewed Research Results
All selected articles conducted empirical experiments in classroom settings and in-
cluded control groups. These experiments primarily aimed to compare team formation
methods supported by innovative algorithms against traditional grouping approaches.
The data presented in Figure 8clearly show that the combination of random and self-
organized grouping methods was the most frequently discussed approach in the litera-
ture, with six studies employing this comparative approach [
10
,
11
,
13
,
15
,
36
,
40
]. Following

Educ. Sci. 2024,14, 675 16 of 24
closely were five studies that compared these methods against instructor-led team for-
mations [
14
,
23
,
35
,
37
,
41
], with some research focusing solely on self-organized or random
grouping as a control. Notably, some studies also engaged in comparisons between al-
gorithms, such as contrasting an improved GA with a simple GA [
32
]. Surprisingly,
experimental validation revealed that all comparative results consistently showed that
the grouping methods supported by the proposed algorithms outperformed traditional
grouping approaches. Surprisingly, experimental validation consistently demonstrated
that the grouping methods supported by the proposed algorithms surpassed traditional ap-
proaches in several key aspects: higher academic performance [
10
,
11
,
13
–
15
,
19
,
35
–
38
,
40
,
41
],
increased satisfaction [
10
,
26
], improved collaborative experiences [
11
,
15
,
25
,
39
] and a posi-
tive impact on student engagement and affections [
23
]. Furthermore, the results from the
improved GA were also superior to those of the simple GA [32].
Figure 8. Team formation methods for control groups are crucial for analyzing how different methods
of team formation affect group dynamics, performance and outcomes. The way teams are formed can
significantly influence the results of studies and the effectiveness of collaborative efforts. For example,
with “self-organized” teams, participants choose their own teams based on personal preferences,
existing relationships or mutual interests. This method is often used to enhance motivation and
satisfaction among participants.
3.3.2. Evaluation
In this review, a thorough analysis of 20 related works was conducted, focusing on
three core dimensions: research design, data collection and data analysis methods. All of
the review studies utilized empirical research methods, with 19 being empirical quantitative
studies and only one being an empirical mixed method study. As shown in the upper left
bar chart in Figure 9, among the 20 articles analyzed, controlled experiments emerged as a
highly preferred method of data collection, being employed in 18 studies.
Following closely were academic performance tests, utilized in 13 articles. Addition-
ally, the survey method was adopted in 12 studies, underscoring its vital role in the data
collection process. The upper right bar chart in Figure 9further reveals the application of
data analysis methods. Notably, three articles [
11
,
23
,
39
] utilized observational methods to
gather qualitative data, which was subsequently analyzed through quantitative techniques,
employing quantitative content analysis. This approach transforms qualitative data, such
as text, into numerical data that can be quantitatively analyzed. By merging the in-depth
insights of qualitative data with the precision of quantitative research, this analysis enables
researchers to systematically and objectively examine qualitative data. Both qualitative
and quantitative analysis methods were utilized, with descriptive statistics (applied in
13 articles) and T-tests (applied in eight articles) being the most common quantitative
data analysis methods. A unique study [
15
] combined qualitative data collection through

Educ. Sci. 2024,14, 675 17 of 24
interviews and qualitative analysis with quantitative data collection through performance
tests and quantitative analysis using variance analysis. Because a single article might cover
many study design categories, it is important to note that the total number of articles
depicted in the bubble charts is more than that in the bar charts. The bubble charts offer a
perspective on the concentration and diversity of research methods. The mix of research
designs and data collection methods is shown in the lower left bubble chart, which suggests
that empirical quantitative research has taken over as the most common methodology.
On the other hand, the combination of methods for data analysis and research design is
displayed in the bubble chart on the right.
Figure 9. Distribution of data collection methods in reviewed studies. The bar chart in the upper left
shows the prevalence of controlled experiments as the data collection method in 18 out of 20 analyzed
articles, highlighting the dominance of this approach in empirical quantitative and mixed method
research designs.
In a study conducted by Oscar et al. [
38
], an experiment was designed to compare
traditional grouping based on student preferences in grouping with an improved GA inte-
grated with the Big Five Inventory theory. During the academic years B-2019 and A-2020,
238 students from the program systems engineering majors at the University of Nariño,
Mariana University and CESMAG University in San Juan de Pasto, Colombia participated
in the experiment, engaging in collaborative learning activities across 14 programming and
related courses. Courses numbered 1–10 were designated as the experimental group, where
students were grouped using an improved GA after taking the Big Five Inventory test.
Courses numbered 11–14 served as the control group, where teams were formed based on
student preferences without the Big Five Inventory test. After grouping, teachers assigned
tasks, and students engaged in collaborative activities, with post-tests conducted for both
the experimental and control groups at the end of the experiment. The students completed
a collaborative project in each course, and after the activities, they filled out a questionnaire
comparing seven collaboration indicators: participation and decision making, conflict
management, problem resolution, internal communication, external communication, collab-
oration and leadership. This study used descriptive statistics’ quantitative data analysis to
find that the collaborative performance of groups predetermined by the suggested strategy
(experimental group) was generally superior to that of the traditionally formed groups (con-
trol group). Furthermore, the experiment revealed that neglecting to consider personality
traits as criteria before forming groups typically resulted in inferior outcomes.
The impact of various grouping techniques on learning outcomes was investigated in a
study by Amara et al. [
41
] by contrasting teacher-organized groups with groups created by
an algorithm. The study involved 54 students from a private secondary school participating
in a collaborative learning project based on the French language. The project required

Educ. Sci. 2024,14, 675 18 of 24
student teams to gather information about a selected city, visit the city together, commu-
nicate with teammates and incorporate photos and videos into their project, culminating
in a report about the chosen city. For this purpose, two types of groups were established;
nine groups of three students each, which were manually created by teachers, served
as the control group, and nine groups of three students each, which were automatically
created with an algorithm, served as the experimental group. Pre-tests and post-tests were
conducted before and after the collaborative activity to assess the learning outcomes. The
quantitative method of analyzing the descriptive data of the above test results led to the
conclusion that at the pre-test stage, the average scores of both groups were nearly identical
and relatively low, reflecting a lack of cognitive ability and knowledge about the written
report topic among students in both the control and experimental groups at the outset.
However, in the post-test phase, although the average scores of both groups improved,
the increase was more significant in the experimental group. This result indicates that the
algorithm-based grouping method had a significant positive impact on students’ learning
outcomes, achieving greater success compared with the teacher-managed control group.
3.4. Education Content
Research on automatic team formation covers a wide range of educational content,
yet the distribution is uneven. As depicted in Figure 10, the data reveal that only 25% of
the studies focused on school education, while 75% were dedicated to higher education,
with 15 studies specifically targeting this sector. Within higher education, the fields of
computer science and engineering dominated, accounting for 50% of the research and
significantly surpassing other educational areas. The specific courses involved include
Programming [
10
,
14
,
25
,
35
,
36
], Data Structures [
12
], Discrete Mathematics [
19
] and Software
Technology [
32
]. This bias towards STEM fields may be attributed to the researchers and
developers themselves being faculty members in computer science or engineering who, for
convenience in sampling, chose their own students as participants. Nonetheless, business
and education majors also held certain proportions at 15% and 10%, respectively, indicating
that research on automatic team formation is gradually expanding to other educational
fields. The courses involved in these fields include the MBA program [
26
] and Modern
Educational Technologies [11].
Figure 10. Algorithm-supported grouping applications across discipline and grade level.
However, compared with the extensive research in higher education, studies at the
school level accounted for only 25%, a relatively low proportion. Some involved courses at
the school level include math problem-solving classes [
23
], French language classes [
41
]
and some activities that require group collaboration in report writing [
15
]. This suggests
that there is significant room for improvement in the application and research of automatic

Educ. Sci. 2024,14, 675 19 of 24
team formation at the foundational education stage. Moreover, given the importance of
teamwork across various majors and courses, especially in the field of medicine and the
arts, encouraging more participation in these areas is particularly crucial. By expanding
the range of majors involved in the research, a more comprehensive exploration and
understanding of the application and effectiveness of automatic team formation technology
across different educational backgrounds can be achieved, thereby fostering innovation
and improvement in educational practices.
In summary, although current research on automatic team formation has achieved
certain results in the field of higher education, it still needs to be expanded to more
educational fields and learning stages, especially those professions that require strong
teamwork skills. Through such expansion, automatic team formation technology can
be more comprehensively evaluated and utilized, thereby enhancing the collaboration
skills and learning outcomes of students across different disciplines and stages of learning.
Additionally, the potential for research at the school level should not be overlooked, and
future studies should continue to shift more focus toward this area.
4. Discussion
In our systematic review, we examined 20 studies exploring how algorithms can be
used to create effective teams for educational settings and identified four key areas for
analysis. These areas included the algorithms themselves, the characteristics that influence
group formation, the research methods used in the studies and the specific technical
methods used in real-world educational settings. In this section, we will also discuss the
challenges and limitations identified in the literature, along with a critical evaluation of the
studies themselves.
4.1. Main Findings of Algorithm-Based Team Formation Research
Our review revealed that Genetic Algorithms (GAs) were the most common algorithm
used for team formation, appearing in 70% (14 out of 20) of the studies. However, simple
GAs can struggle with large and complex problems, often getting stuck on solutions that are
not the best (premature convergence). To overcome this limitation, researchers developed
enhanced GAs. These improved algorithms incorporate better genetic operators and fitness
functions, allowing them to explore a wider range of possibilities and find better solutions.
This is achieved by introducing new variations within the groups (genetic information) and
preventing the algorithm from getting stuck in local minima (i.e. suboptimal solutions).
The studies showed that these enhanced GAs outperform simple GAs. They consis-
tently found higher quality solutions and could handle a larger number of factors when
forming groups. Importantly, they achieved this within a reasonable amount of time for
most real-world applications.
However, there is still room for improvement. One area of focus is optimizing the
GA model itself. For example, a study proposed a “feature categorization model”—an
enhanced GA specifically designed to address the challenge of forming groups when there
is little initial data (cold start problem) [11].
Nevertheless, a number of investigations have looked into integrating GAs with other
algorithms to improve the grouping results. This multidisciplinary strategy makes better
use of the advantages of several algorithms to tackle challenging issues. For instance, the
Team Machine in Berge Mark’s [
37
] study uses GAs and the GRASP. This method seeks
to refine and improve the most effective solutions discovered while also supporting the
thorough investigation of possible team configurations through the synergistic use of both
algorithms. Additionally, to facilitate the trade-off of multi-objective grouping optimization,
Lin et al. [
36
] proposed a novel approach based on the enhancement of a GA with the
Technique for Order Preference by Similarity to Ideal Solution. Based on the proposed
approach, further development was carried out on a web-based group support system to
assist educators. Furthermore, researchers have been actively investigating and utilizing a
range of other algorithms in addition to GAs. Six papers in this review used non-genetic

Educ. Sci. 2024,14, 675 20 of 24
algorithm approaches: the variable neighborhood search algorithm, the k-means algorithm,
the cluster and prune method and minimum entropy collaborative grouping. As a result,
the varied selection of algorithms in these works offers a wider view and possible solutions
for dealing with particular grouping issues.
Next, the grouping type and criteria are also key elements in the grouping process. In
terms of grouping type, six studies used only heterogeneous grouping in the algorithmic
and experimental phases, while the least number of studies used homogeneous grouping ex-
clusively at only three. The algorithmic phase of the studies considered both homogeneous
and heterogeneous grouping criteria in the highest number of studies, amounting to 11.
However, when these studies were applied to the experiments, three used homogeneous
intergroup grouping but heterogeneous intragroup grouping, two used homogeneous
grouping for some characteristics but heterogeneous grouping for others, and six used ho-
mogeneous, heterogeneous or mixed grouping. This finding reveals that during the group
formation process, researchers tend to explore and experiment with different grouping
types to find the most appropriate grouping strategy for a particular learning environment
and goal.
In this review, grouping criteria were divided into static and dynamic types. Static char-
acteristics refer to attributes that remain unchanged over a short period, such as learning
styles, previous academic performance, learning roles, gender, age, major, programming
skills, leadership levels, knowledge levels, communication skill levels and personality
traits. Studies that chose static characteristics as grouping criteria accounted for the highest
proportion, reaching 55%. This may be because these characteristics are relatively stable
and easy to measure, making them the preferred choice in educational grouping research.
Another possible reason is that static characteristics cover a wide range, and most of these
traits are commonly used standards for grouping. Dynamic characteristics, on the other
hand, refer to attributes that never change or at least change quite slowly, including social
interactions and emotional states. Studies utilizing dynamic characteristics occurred the
least, accounting for only 15% of the studies. Research that considered both static and
dynamic characteristics was also a trend, with 25% of the studies incorporating information
such as students’ social levels and social networks into the grouping criteria on top of static
characteristics. This classification method reveals how researchers choose grouping criteria
based on the stability and measurability of characteristics in group formation studies. More-
over, studies that combine static and dynamic characteristics indicate that considering the
multidimensional traits of students is necessary for a more comprehensive understanding
and addressing of the complexity of learning groups. These diversified grouping criteria
not only help form more efficient and cohesive learning groups but also provide more
flexible and detailed grouping strategies for educational practices.
To gain a deeper understanding of the research designs involved in the literature, this
review conducted a detailed analysis and summary in terms of three aspects: data collec-
tion methods, data analysis methods and research methods. In terms of data collection
methods, comparative experiments (N = 18), exams (N = 13) and surveys (N = 12) were
the primary tools. For data analysis methods, descriptive statistics (N = 13) and T-tests
(
N=8
) were predominant. Regarding the choice of research methods, 19 studies employed
empirical quantitative research methods, while one study utilized a mixed research method
that combines both quantitative and qualitative approaches. The widespread application
of empirical quantitative research reflects the emphasis on data-driven decision making
and evidence-based practices in educational research. The adoption of mixed research
methods further enriched the depth and breadth of the research, enabling researchers to
comprehensively understand and explain research phenomena in terms of multiple dimen-
sions. In comparison with traditional grouping methods (such as random grouping and
self-organizing teams), all studies reached a unanimous conclusion: Algorithm-based team
formation methods have shown more positive results. This finding not only highlights
the potential of algorithms in enhancing the efficiency and effectiveness of team formation
but also provides valuable references for future educational practices and research. Some

Educ. Sci. 2024,14, 675 21 of 24
subjects in higher education, like science and engineering, education and business, have
benefited from those team formation approaches. By adopting more scientific and system-
atic approaches to team formation, it is possible to effectively improve learning outcomes
and the quality of team collaboration, thereby promoting innovation and development in
more educational areas.
4.2. Challenges and Limitations of Algorithm-Based Team Formation Research and
Future Directions
Although numerous studies consistently indicate that technology-assisted grouping
methods significantly outperform traditional manual grouping in terms of efficiency and
effectiveness, traditional grouping strategies still dominate in real-world educational set-
tings. The reasons behind this phenomenon are complex and varied. Firstly, most of these
innovative grouping algorithms remain in the initial stages of research and development
and have seldom developed user-friendly mature products. Secondly, even if some research
results have been successfully transformed into applications, their user experience may
not be as intuitive or convenient. Moreover, the promotion and marketing activities of
these applications may not have achieved the expected results, failing to attract sufficient
attention from educators and learners, which in turn limits their widespread adoption and
application. If the algorithm or platform is not user-friendly enough, then it may limit
accessibility and utilization in real-world educational contexts. That aside, challenges may
arise in data collection, processing and privacy protection as well due to the utilization of
extensive student data, such as personal characteristics, academic performance and prefer-
ences. Therefore, despite the great potential demonstrated by technology-assisted grouping
methods in theory and experiments, overcoming numerous challenges, including enhanc-
ing product usability, strengthening user training and implementing effective marketing
strategies, is necessary before they can replace traditional methods in practical applications.
Therefore, although technology-assisted grouping methods have shown great poten-
tial in theoretical research and experimental validation, replacing traditional grouping
methods in practice faces numerous challenges. These include fostering interdisciplinary
collaboration among the psychology, education, computer science and engineering fields to
jointly develop a comprehensive product suitable for most collaborative learning scenarios.
Additionally, enhancing product user-friendliness, strengthening user training and support
and implementing effective marketing strategies are also crucial steps toward achieving
this goal.
5. Conclusions
We provided a comprehensive overview of the development and application of auto-
matic group formation techniques to enhance teamwork and collaboration in collaborative
learning. Automated team creation, supported by advanced algorithms, has emerged as
a significant area of focus within collaborative learning. Manually generating effective
groups is a complex task, but several techniques are available to automate this process.
We examined the different algorithms used, the types of groups being formed, the criteria
considered for grouping, and the educational content involved. We also reviewed the
experimental designs of these studies, including how data was collected and analyzed and
the methods used to evaluate the success of the algorithms. By analyzing these aspects, we
aimed to identify gaps in current research, encourage further exploration in this field and
provide insights for future research.
Research shows that the most popular team formation algorithm was the Genetic
Algorithm (GA), which was used in 14 studies, accounting for 70% of the works. Enhanced
GAs improve the performance and accuracy of algorithms through improvements in genetic
operators and fitness functions. In terms of grouping type, we found that researchers tend
to explore and experiment with different grouping types to find the most appropriate
grouping strategy for a particular learning environment and goal. Studies that chose static
characteristics such as knowledge level, learning roles and personality traits as grouping

Educ. Sci. 2024,14, 675 22 of 24
criteria accounted for the highest proportion, reaching 55%. However, there is a growing
trend of considering both static and dynamic characteristics, such as social interactions, to
create well-rounded teams.
The main methods for gathering data are surveys (N = 12), exams (N = 13) and
comparative experiments (N = 18). T-tests (N = 8) and descriptive statistics (N = 13) are
the most common data analysis techniques. Algorithm-based team building techniques
have demonstrated more successful outcomes when compared with traditional grouping
approaches (such as random grouping and self-organizing teams) according to all studies
that conducted this comparison. Notably, science and engineering, education and business
have benefited from automatic team formation approaches. Those findings highlight the
potential of efficient and effective team formation approaches. In addition, they provide
valuable references for future educational practices and research as well.
Although technology-assisted grouping techniques have demonstrated significant
promise in theoretical investigation and experimental validation, their application in real-
world scenarios faces numerous challenges, such as developing user-friendly and compre-
hensive products, promotion and marketing of products and privacy protection. For future
improvements, fostering interdisciplinary collaboration among psychology, education,
computer science and engineering should be encouraged to jointly develop a compre-
hensive product suitable for most collaborative learning scenarios. Likewise, improving
the usability of the product, boosting customer support and training and implementing
efficient marketing plans are all essential stages.
A limitation of this review is that it only included analyses of 20 journal articles,
excluding conference papers, pre-print articles and books. This restriction means that
the analysis covered a narrower range of the research field than if these other sources
had been considered. However, our findings suggest that despite the limited application
of team formation systems in current collaborative learning settings, their potential and
attractiveness still merit further attention and investigation.
Author Contributions: X.W. prepared the original article draft. Both X.W. and G.S. reviewed and
analysed the literature and developed the figures. Article conceptualisation, review, and editing were
performed by R.G., who also secured funding and resources. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was financially supported by the UK’s Engineering and Physical Sciences
Research Council (Project Number: 323668, Project Name: “ARTIFY”).
Data Availability Statement: All results analysed during the current study are derived from publicly
accessible sources and all data are included in this article. Additional details can be requested from
the corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
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