Low-performing Students Research Articles

Secondary School Students’ Enacted Self-Regulated Learning Strategies in a Computer-Based Writing Task–Insights from Digital Trace Data and Interviews

AbstractStudying in digital learning environments highlights the skills needed to regulate one’s own learning. In youth, students are acquiring and developing these skills, but for many students, effectively self-regulating their learning is challenging. To design support in this regard, an in depth understanding of how and why their self-regulated learning (SRL) is enacted is needed. This study focuses on secondary school students’ enacted SRL strategies in a computer-based, multi-source writing task to detect and explain differences between high- and low-performing students. To address this aim, the students’ SRL processes during the task were captured using digital trace data (N = 50, navigational log, mouse, and keyboard data) and supplemented with stimulated recall interviews (n = 17). Raw trace data were parsed by implementing an existing theory-based process library that automatically detects the SRL processes. The durations and network properties of and transitions between the SRL processes of students in the highest and lowest essay score tertiles were investigated, involving the novel application of network metrics, and a qualitative content analysis for the stimulated recall data was performed. The results show that successful students differed from less successful ones regarding the time they allotted for the SRL processes, the number of distinct transitions between them, as well as transition probabilities. The successful students expressed a larger proportion of and different cyclical patterns of SRL processes during the task. The student interviews contextualise these findings and complement them by revealing qualitative differences in students’ monitoring of learning. This study provides novel insights into SRL among young students in computer-based writing task and suggests approaches for designing effective, personalised support for students’ adaptive learning strategies, which can be useful in developing educational technology and teacher education.

Empowering Discovery Learning and Direct Instruction with Computer-Assisted to Enhance Mathematical Performance

In the current educational context, the low academic performance of students in mathematics indicates the need for the implementation of more effective teaching methods to support the understanding of complex concepts. The effectiveness of learning can be enhanced through computer-assisted instruction, particularly in discovery learning (DL-CA) and direct instruction (DI-CA) utilizing web-based media. This study employs a quantitative approach with statistical analysis to examine the significant impact of these teaching methods on students' mathematical performance. Discovery learning and direct instruction were applied to two groups studying numerical methods, focusing on the topic of nonlinear equation solutions using the bisection method, with each group consisting of 33 students. The statistical test results indicated that there was no significant difference in mathematical performance between the DL-CA and DI-CA groups; both methods showed comparable effectiveness. However, both methods had a significant effect on the N-Gain of the learning outcomes in both groups. The improvement in mathematical performance for the DL-CA group was better than that for the DI-CA group, with more students showing high improvement (11 students) compared to the DI-CA group (1 student). Additionally, in the low improvement category, the DL-CA group had fewer students (2 students) than the DI-CA group (8 students). This suggests that both discovery learning and direct instruction have almost equivalent reliability in achieving students' mathematical abilities, but DL-CA is more effective in enhancing performance compared to DI-CA.

Development of augmented reality media based on Assemblr Edu to enhance the learning outcomes

This study focuses on the development and evaluation of augmented reality (AR) learning media based on the Assemblr Edu application, aimed at enhancing the learning experience of fifth-grade students at SDN Tambaharjo 01. The AR media covers the subject of Community Economic Conditions and Natural Resources and Supporting Factors for Community Economic Activities. The research process included designing the AR content, validating it with experts, and conducting small and large group trials. This study investigates the development and effectiveness of AR learning media based on the Assemblr Edu application to improve learning outcomes in Natural Science and Social Studies for fifth-grade students at SD Negeri Tambaharjo 01. The research employs a quantitative approach with a Research and Development (R&D) method, utilizing the ADDIE development model to create and assess the learning media. The study addresses low student performance in Natural Science and Social Studies by integrating interactive AR technology to make learning more engaging and effective.The developed AR media, which includes 3D and 2D interactive content, was validated by material and media experts, yielding a high validity score of 90.31%. Small and large group trials were conducted, showing significant improvements in student learning outcomes. Pretest and posttest results demonstrated an average score increase from 50 to 85, with a student response rate of 88.46% indicating the media's feasibility. Teachers also rated the AR media highly, with a response score of 91.34%, confirming its practical application in educational settings. The study concludes that the Assemblr Edu-based AR learning media is highly feasible and effective in enhancing student learning outcomes in Natural Science and Social Studies.

Exploiting the Regularized Greedy Forest Algorithm Through Active Learning for Predicting Student Grades: A Case Study

Student performance prediction is a critical research challenge in the field of educational data mining. To address this issue, various machine learning methods have been employed with significant success, including instance-based algorithms, decision trees, neural networks, and ensemble methods, among others. In this study, we introduce an innovative approach that leverages the Regularized Greedy Forest (RGF) algorithm within an active learning framework to enhance student performance prediction. Active learning is a powerful paradigm that utilizes both labeled and unlabeled data, while RGF serves as an effective decision forest learning algorithm acting as the base learner. This synergy aims to improve the predictive performance of the model while minimizing the labeling effort, making the approach both efficient and scalable. Moreover, applying the active learning framework for predicting student performance focuses on the early and accurate identification of students at risk of failure. This enables targeted interventions and personalized learning strategies to support low-performing students and improve their outcomes. The experimental results demonstrate the potential of our proposed approach as it outperforms well-established supervised methods using a limited pool of labeled examples, achieving an accuracy of 81.60%.

The effectiveness of Gen AI in assisting students’ knowledge construction in humanities and social sciences courses: learning behaviour analysis

ABSTRACT Currently, generative AI has undergone rapid development. Numerous studies have attested to the benefits of Gen AI in programming, mathematics and other disciplines. However, since Gen AI mostly uses English as the intrinsic training parameter, it is more effective in facilitating the teaching of courses that use international common notation, but few scholars have researched the fitness of Gen AI-assisted teaching of humanities courses in Chinese-language environments. To address these gaps, this study examined the learning behaviours of 30 students using Gen AI to help them answer questions on economic law tests using the Lag Sequential Analysis. The results show that the following: (1) The use of Gen AI to aid learning in an economic law course did not significantly improve the cognitive level of academics from the perspective of knowledge construction. (2) According to the characteristics of students’ behavioural paths via Gen AI-assisted learning, their behavioural patterns can be classified into autonomous and innovative, moderate, and lacking innovation. (3) Different learning modes when Gen AI-assisted teaching was used affected the final results, which were as follows: High-performing students favoured the autonomous and innovative pattern, medium-performing students favoured the moderate pattern, and low-performing students favoured the lacking innovation pattern.

Unskilled and unaware? Differences in metacognitive awareness between high and low-ability students in STEM

IntroductionMetacognition, or the ability to monitor and control one's cognitive processes, is critical for learning in self-regulated contexts, particularly in introductory STEM courses. The ability to accurately make predictions about one's ability and performance can determine the effectiveness in which students effectively prepare for exams and employ good study strategies. The Dunning-Kruger pattern, where low-performing individuals are more overconfident and less accurate at the ability to predict their performance than high-performing individuals, is robustly found in studies examining metacognitive monitoring. The extent to which the Dunning-Kruger pattern can be explained by the lack of metacognitive awareness is not yet established in the literature. In other words, it is unclear from prior work whether low-performing students are “unskilled and unaware” or simply “unskilled but subjectively aware.” In addition, arguments about whether this pattern is a psychological phenomenon or a statistical artifact of the measurement of metacognition can be found in the literature.MethodsStudents enrolled in three different physics courses made predictions about their exam scores immediately before and after taking each of the three exams in the course. Student predictions were compared to their exam scores to exam metacognitive accuracy. A new method for examining the cause of the Dunning-Kruger effect was tested by examining how students adjust their metacognitive predictions after taking exams.ResultsIn all contexts low-performing students were more overconfident and less accurate at making metacognitive predictions than high-performing students. In addition, these students were less able to efficiently adjust their metacognitive predictions after taking an exam.DiscussionThe results of the study provide evidence for the Dunning-Kruger effect being a psychological phenomenon. In addition, findings from this study align with the position that the skills needed to accurately monitor one's performance are the same as those needed for accurate performance in the first place, thus providing support for the “unskilled and unaware” hypothesis.

Exploring Debugging Challenges and Strategies Using Structural Topic Model: A Comparative Analysis of High and Low-Performing Students

Debugging is essential for identifying and rectifying errors in programming, yet time constraints and students’ trivialization of errors often hinder progress. This study examines differences in debugging challenges and strategies among students with varying computational thinking (CT) competencies using weekly coding journals from an online undergraduate CT course. Participants used Scratch, a block-based programming language, and their journals from five assignments were analyzed using Term Frequency-Inverse Document Frequency and Structural Topic Modeling. High-performing students engaged with diverse topics and specific blocks tied to their weekly projects while low-performing students faced repetitive and broad challenges, such as understanding motion blocks and broadcast concepts. These patterns reveal that low-performing students struggle particularly during the ‘diagnose the fault’ phase of debugging, often hindering their progress in the final stage. Such challenges highlight the necessity for targeted educational interventions to improve the debugging proficiency and overall CT skills. The study underscores the importance of further research into students’ logical thinking processes during code review and debugging, suggesting the use of think-aloud protocols and detailed tracking of debugging practices for deeper insights. This research contributes to the field by showing that differentiated instruction and strategic support can enhance debugging skills across different student performance levels.

Identifying the Determinants of Academic Success: A Machine Learning Approach in Spanish Higher Education

Academic performance plays a key role in assessing the quality and equity of a country’s educational system. Studying the aspects or factors that influence university academic performance is an important research opportunity. This article synthesizes research that employs machine learning techniques to identify the determinants of academic performance in first-year university students. A total of 8700 records from the Complutense University of Madrid corresponding to all incoming students in the academic year 2022–2023 have been analyzed, for which information was available on 28 variables related to university access, academic performance corresponding to the first year, and socioeconomic characteristics. The methodology included feature selection using Random Forest and Extreme Gradient Boosting (XGBoost) to identify the main predictors of academic performance and avoid overfitting in the models, followed by analysis with four different machine learning techniques: Linear Regression, Support Vector Regression, Random Forest, and XGBoost. The models showed similar predictive performance, also highlighting the coincidence in the predictors of academic performance both at the end of the first semester and at the end of the first academic year. Our analysis detects the influence of variables that had not appeared in the literature before, the admission option and the number of enrolled credits. This study contributes to understanding the factors that impact academic performance, providing key information for implementing educational policies aimed at achieving excellence in university education. This includes, for example, peer tutoring and mentoring where high- and low-performing students could participate.

Predictive Model for Identification and Analysis of Factors Impacting Students Academic Performance Using Machine Learning Algorithms

Background: Education is a vital component of both societal and individual growth. To create effective learning environments, it is essential to understand the factors that influence student achievement. However, the application of machine learning algorithms can drive positive change, providing higher education institutions with effective solutions to their challenges. Aim: This research develops a predictive model to identify and analyze the factors affecting student academic performance, aiming to provide institutional administrators and lecturers with a better understanding of the key factors impacting student success. Method: A comprehensive dataset was collected from undergraduate students at Modibbo Adama University (MAU), Yola, comprising student-related, home-related, lecturer-related, and institution-related factors. Model development was carried out using Python in Google Colab, a cloud-based Jupyter notebook environment. Classification algorithms such as K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Method (GBM), and Random Forest (RF) were applied to predict student academic performance. Four evaluation metrics namely; accuracy, precision, recall, and f1-score were used to analyze the models. Results: The Random Forest model outperformed the other machine learning models, achieving an overall accuracy of 95%. For predicting low-performing students, the model achieved a precision of 0.9677, recall of 0.9474, and f1-score of 0.9574. For high-performing students, the precision was 0.9324, recall was 0.9583, and f1-score was 0.9452. These strong performance metrics across both low and high performing student groups demonstrate the effectiveness of the Random Forest model in accurately predicting student academic performance based on the factors considered in the study. Feature importance analysis identified lecture attendance, sponsor support, quality of facilities, and lecturer clarification as the most influential factors on student performance. Other features, such as accommodation, employment status, and program preference, were found to have a low impact. These findings emphasize the importance of considering a comprehensive set of student, lecturer, institution, and home-related factors to sustain a conducive learning environment and enhance educational practices.

Self-assessment skills of undergraduate students in operative dentistry: Preclinical performance and gender.

Self-assessment is key to improving the quality of work performed by dental professionals. The ability to accurately self-assess has been found to correlate with better clinical performance, making it an important skill for students to master during their dental education. Furthermore, studies conducted in dental schools across the world have shown that lower-performing students tend to overestimate their abilities compared to their peers. This study aimed to evaluate the self-assessment skills of dental students in pre-clinical operative dentistry and to investigate the impact of gender on these skills. Third-year undergraduate dental students (N = 335) took 2 pre-clinical practical exams: class II composite preparation and restoration. Students self-assessed each assignment using a standardized rubric, and 6 calibrated faculty members graded all procedures blindly and independently. The difference between students' self-assessment scores (S) and mean faculty grades (F) reflected the students' self-assessment skills and was referred to as the Student-Faculty (S-F) gap. A positive S-F gap indicates that students overestimate their work, while a negative S-F gap indicates that students underestimate their work. Data was stratified by gender and by faculty-determined student performance, and then statistically analyzed. The study demonstrated a statistically significant difference between faculty grades and students' self-assessment scores. Positive S-F gaps were observed across all procedures, indicating overestimation, with a mean S-F gap of 11.4 ±9.9%. A negative correlation was found between faculty grades and S-F gaps. Higher-performing students showed smaller S-F gaps (4.8 ±5.3%) compared to lower-performing students (21.2 ±9.68%). Furthermore, male students showed significantly higher S-F gaps (14.0 ±10.3%) compared to females (9.7 ±9.4%). Overestimation was more prevalent among lower-performing and male students compared to higher-performing and female students. Future investigations should consider exploring effective interventions and educational strategies aimed at improving students' self-awareness and their ability to accurately assess their performance.

Generative AI in student English learning in Thai higher education: More engagement, better outcomes?

Despite the growing body of research exploring artificial intelligence (AI) in educational contexts, there remains a critical gap in understanding the specific effects of generative AI (GAI) on English language learning, particularly within higher education in Thailand. This study addresses this gap by employing an explanatory sequential mixed-method design to explore both student and teacher perspectives. Quantitative data were analyzed using descriptive statistics, Mann-Whitney U tests, Spearman correlation analysis, and Kendall–Theil regression to examine students' acceptance, usage, and the relationship between GAI engagement and academic performance. Qualitative data, collected through structured written interviews, were analyzed using thematic analysis to uncover key themes from both student and teacher narratives. Quantitative findings reveal high student acceptance of GAI tools in terms of performance expectancy (M = 3.66, SD = .58), effort expectancy (M = 3.61, SD = .59), facilitating conditions (M = 3.51, SD = .59), and use behavior (M = 3.52, SD = .48), while social influence remained relatively lower (M = 3.31, SD = .54). Mann-Whitney U tests showed no statistically significant differences in GAI usage across high- and low-performing students (p > .05). Correlation analyses indicated strong associations between performance expectancy and other factors (ρ = .87, p < .001), yet no significant correlation with GPA (ρ = −.06, p = .76). Qualitative results from student reflections revealed improved efficiency, enhanced engagement, and increased linguistic confidence. Yet, concerns about overreliance on AI and the necessity for critical use were noted. Teacher narratives accentuated ethical concerns and the potential for GAI misuse, highlighting the need for balanced, responsible integration of AI into pedagogical practices. These findings underscore the potential of GAI to enhance learning experiences while emphasizing the importance of maintaining academic integrity and fostering critical thinking.

Reflective Questions: Promoting Metacognition Through Discussion Board Prompts

During two semesters, a numerical methods course for mechanical engineering students at a large US southeastern university used discussion board questions to promote reflection and metacognition. The course covered eight chapters, each with a related discussion question. The students could choose to answer these questions and receive 2% extra credit for the course. This was intended to help the students who missed some of the 30 online homework assignments that comprised 15% of the final course grade. The questions were also meant to encourage the students to think deeply and creatively. The students could see other students’ responses after they posted their own. The questions ranged from making a meme, writing a nursery rhyme, and explaining a complex or easy concept. Only 64% of the total possible responses were submitted by students, and there was a small-to-medium practical but no statistical significance between the levels of participation among the high- or low-performing students. The submissions were analyzed and determined to be at the low level of Bloom’s taxonomy. They identified complex topics to inform future instruction.

Penerapan Model PBL Melalui Pendekatan Deferensiasi untuk Meningkatkan Hasil Belajar Pendidikan Pancasila Materi Gotong Royong pada Kelas IV di SDN Dukuh Kupang II Surabaya

This study is motivated by the low academic performance of fourth-grade students in Pancasila Education, specifically in the topic of mutual cooperation. The aim of this research is to assess the improvement in learning outcomes for fourth-grade students in Pancasila Education through the implementation of the Problem Based Learning (PBL) model with a Differentiation approach. This research falls under the category of classroom action research and was conducted in two cycles. The subjects of the study were 28 fourth-grade students from SDN Dukuh Kupang II Surabaya. Data were collected through observations and tests, and then analyzed using descriptive quantitative methods. The results indicate that the implementation of the PBL model with a Differentiation approach successfully improved student learning outcomes in the topic of mutual cooperation. The average student performance was 57.14 during the pre-cycle. After implementing the PBL model in cycle I, the average score increased to 71.42, and in cycle II, it rose further to 84.72. Based on these results, it can be concluded that the PBL model with a Differentiation approach is effective in enhancing the learning outcomes of fourth-grade students on the topic of mutual cooperation at SDN Dukuh Kupang II Surabaya.

Learning and Study Skills in Students at a Small Liberal Arts College

Despite a large body of literature on the various study skills that predict academic performance in college students, there is no consensus on which specific study skills contribute the most toward college success. Additionally, the emphasis in the literature is primarily on large public universities and specialized groups such as chiropractic, medical, pharmacy, and vocational students, with little to no research focused on small liberal arts institutions. Finally, studies in the field typically assess all students, and use a wide range of GPAs in examining differences between high- and low-performing students. It is important to see if differences in study strategies exist even when only a small subset of students are tested – many of whom are on probation for already low academic performances. The present study assessed ten different learning and study skills via the Learning and Study Skills Inventory (LASSI) in 41 students enrolled in a general studies class at a small liberal arts college. Academic achievement was assessed via cumulative GPA for each student. There were significant correlations between individual scores on the motivation subscale and GPA, and between utilizing academic resources and GPA. Both motivation and utilizing academic resources were found to also significantly predict GPA. Theoretical and practical implications of these findings are discussed, in the context of enhancing student success via early and proactive interventions.

A Predictive Model for Academic Performance of Students in MAT101 at Federal University Birnin Kebbi

The research addresses the problem of low academic performance of students taking General Mathematics 1 (MAT101) at the Federal University Birnin Kebbi in Nigeria. MAT101 is a foundational course for several science-related disciplines. However, many students fail to understand its concepts, translating to one of the lowest academic performances, leading to high discontinuation of students’ studies in most science and related fields. To this end, the problem is addressed through a predictive model using data science and machine learning techniques to identify students who are academically at risk. It seeks to answer the appropriateness of the selected statistical and data mining approaches, whether or not they are predictor variables, and validating the model developed. The significance of this study lies in its potential to help instructors identify at-risk students and improve overall performance in MAT101. The research uses data collected from academic records and conducts exploratory data analysis to determine the factors influencing students’ performance. Several classifiers such as Decision Tree and Random Forest were analyzed by evaluating their accuracy. In detail, the difference in the accuracy for the Decision Tree algorithm was 52% and for the Random Forest was 67%. Based on achieved indicators, the Random forest algorithm is more effective in this type of predictive modeling. However, the choice of algorithm depends on specific task requirements, considering factors like speed and accuracy.

Metacognitive Skills in STEM Integrated Discovery Learning Models for Kinetic Gas Theory in High School

Metacognitive skills are an important component supporting mastery of concepts in studying physics. This research aims to describe students' metacognitive skills through STEM-integrated discovery learning on kinetic gas theory material at SMAN 1 Dolopo. This research uses the One-Group Pretest-Posttest Design method, where in this design, one experimental group is selected to measure students' metacognitive abilities. The research on the kinetic theory of gas metrics was carried out in class XI Merdeka 4. The research took place in the odd semester of 2022-2023. The results of students' metacognitive skills show an increase in every metacognitive aspect, namely planning skills. The increase occurred in every meta-cognitive aspect, namely planning skills, which were originally 66.4 to 73.7. Monitoring skills, which previously received a score of 60.4, also changed to 73. Evaluating skills possessed by students also changed, from 64.4 to 67.4. Significant increases occurred from the pretest to the posttest, especially in the monitoring skills category. From the table, it is known that the increase occurred from the moderately developed to the developed category. Based on the research and discussion results, the metacognitive skills of class XI Merdeka 4 students are included in the developing category after receiving STEM-integrated discovery learning. This is in line with research that states that the inquiry learning model, including the discovery learning model, positively affects students' metacognition in learning. High-performing students may have better metacognitive skills than low-performing students. High-performing students may have better metacognitive skills than low-performing students.

DIGITAL BADGES AS REWARDS IN SCIENCE EDUCATION: STUDENTS’ PERCEPTIONS AND EXPERIENCES

Several studies have reported positive effects of digital badges on students’ academic performance and behavior. A digital badge serves as an indicator of an accomplishment or skill that can be showcased, accessed, and verified online. The researcher investigated how students perceive digital badges as rewards in a biology course through a phenomenological study. Gamified lesson plans and students’ portfolios were used to implement gamification and award digital badges. The study involved Filipino Grade 12 STEM students (n = 41) from a single intact class, and interviews were conducted with a subset of 10 students. By analyzing student responses in their portfolios and interviews, four key themes of digital badges were identified: class participation, learning outcomes, time management, and social interaction. The results revealed that these themes were influenced by factors such as competition, a sense of accomplishment, self-improvement, status recognition, and evidence of achievement. It was found that, while digital badges can be beneficial for high-performing students, they may have detrimental effects on low-performing students. The researcher recommended that the identified themes and their underlying drivers be considered when developing digital badges and in future studies on digital badges and gamification.

Interactive Multimedia Based on Project Based Learning Model Using Articulate Storyline 3 Material for Fifth-Grade Elementary School

This study addressed the need for more interactive media in the learning process, which has led to low student performance. The goal was to develop Project-Based Learning (PjBL) model-based Interactive Multimedia using Articulate Storyline 3 for Water Cycle material in Grade V Elementary School that is feasible, practical, and effective in enhancing student learning outcomes. The research followed the ADDIE model: 1) analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. Subjects were Grade V students, and the focus was on their learning outcomes for the Water Cycle material. Data collection involved quantitative observation and tests using rating scale sheets and multiple-choice tests. Data analysis included average score analysis for feasibility and practicality and a correlated t-test for effectiveness. Results indicated that: (1) the average feasibility score of the media was 4.76, and the material test score was 4.63, both rated very high; (2) the average practicality test score was 4.70 by teachers and 4.97 by students, both rated very good; and (3) the significance value in the one-sample t-test was 0.000, showing the PjBL model-based Interactive Multimedia with Articulate Storyline 3 effectively improved student learning outcomes for the Water Cycle material in Grade V. Thus, the research concludes that Interactive Multimedia is feasible, practical, and effective in enhancing student learning outcomes for the Water Cycle material in Grade V Elementary School.

Identification of parameters for electronic distance examinations.

This study investigates the log data and response behavior from invigilated in-person electronic timed exams at the University of Veterinary Medicine Hannover, Foundation, Germany. The primary focus is on understanding how various factors influence the time needed per exam item, including item format, item difficulty, item discrimination and character count. The aim was to use these results to derive recommendations for designing timed online distance examinations, an examination format that has become increasingly important in recent years. Data from 216,625 log entries of five electronic exams, taken by a total of 1,241 veterinary medicine students in 2021 and 2022, were analyzed. Various statistical methods were employed to assess the correlations between the recorded parameters. The analysis revealed that different item formats require varying amounts of time. For instance, image-based question formats and Kprim necessitated more than 60 s per item, whereas one-best-answer multiple-choice questions (MCQs) and individual Key Feature items were effectively completed in less than 60 s. Furthermore, there was a positive correlation between character count and response time, suggesting that longer items require more time. A negative correlation could be verified for the parameters "difficulty" and "discrimination index" towards response time, indicating that more challenging items and those that are less able to differentiate between high- and low-performing students take longer to answer. The findings highlight the need for careful consideration of the ratio of item formats when defining time limits for exams. Regarding exam design, the literature mentions that time pressure is a critical factor, since it can negatively impact students' exam performance and some students, such as those with disabilities, are particularly disadvantaged. Therefore, this study emphasizes finding the right time limits to provide sufficient time for answering questions and reducing time pressure. In the context of unsupervised online exams, the findings of this study support previous recommendations that implementation of a stringent time limit might be a useful strategy to reduce cheating.

Profiles of Occupational Therapy Students: A Cluster Analysis

While studies have examined predictors of study performance in various student groups, cluster analytic studies identify groups of students with similar characteristics. The purpose of this study was to explore relevant clusters of occupational therapy students and examine profile differences between participants in different clusters. A total of 177 first-year students from six occupational therapy programs in Norway participated in this study. Data on age, gender, study approaches, study effort, and study performance were collected. A two-step cluster analysis was conducted. Three clusters were identified. Cluster 1, the high-strategic high-performing students, comprised the successful students, mostly females, who invested much effort and used productive approaches to studying. Cluster 2, the high-surface average-performing students, consisted of less successful female students, who used poor study strategies and made little effort. Cluster 3, the low-strategic low-performing students, comprised the least successful students, who were all male, with study efforts and study strategies in the middle range. Overall, this study suggests that occupational therapy students can be classified into clusters based on a combination of measures. To enhance student learning and performance, educators should pay particular attention to male students and to students investing little effort and using poor study strategies.