Learner Behavior Research Articles

Unpacking interactions among student teams in a practice-based IPE setting: a qualitative evaluation study

ABSTRACT In many healthcare settings, teams change composition regularly, so healthcare students must be trained to function effectively in dynamic teams before entering the workforce. Interprofessional clinical rotations provide an ideal venue for learners to practice these skills, but little is known about how student teams interact in such settings. In this qualitative observational evaluation, learners from multiple health professions at a single institution participated in scheduled clinics in low-income housing communities for older adults. Interprofessional student teams met with program participants for care coordination, health and wellness assessments, and assistance in setting and achieving health goals; team composition changed from week-to-week. A purposive sample was selected from video-recorded encounters between student teams and their program participants. The aim of this study was to explore team interactions and document learner behaviors. Two researchers independently reviewed discrete segments of each video, recorded their observations and reflections, and then the team discussed, categorized, and identified relevant examples of both effective and ineffective behaviors. Four major themes were observed: inclusiveness, leadership, joy of practice, and sharing of clinical knowledge. Students demonstrated both positive and negative examples of behaviors that aligned with each theme. Understanding how students behave on teams in dynamic settings where patient care is taking place can help educators establish practice-based interprofessional education models that better prepare learners to function effectively and strategies that may improve team interactions.

Practices of Teachers in Managing the Behavior of Early Childhood Learners in the Division of Antipolo

This manuscript described the Practices of Teachers in Managing the Behavior of Early Childhood Learners in the Division of Antipolo. It opened the awareness of teachers in dealing with various disruptive behavior, misbehavior and recurring behavior and misbehavior of learners. This study utilized the Quantitative Research Design in gathering data from kindergarten teachers in the Division of Antipolo. This research came up with a proposed seminar and training to continuously enhance the teaching process of early childhood teachers especially in managing the behavior of their learners as it is the first and essential requisite in establishing a better teaching and learning process.

MEPSI: An MDL-Based Ensemble Pruning Approach with Structural Information

Ensemble pruning that combines a subset of individual learners generated in parallel to make predictions is an important topic in ensemble learning. Past decades have developed a lot of pruning algorithms that focus on the external behavior of learners on samples, which may lead to over-fitting. In this paper, we conjecture that the generalization performance of an ensemble is not only related to its external behavior on samples but also dependent on the internal structure of individual learners. We propose the general MEPSI approach based on Kolmogorov complexity and the Minimum Description Length (MDL) principle, which formulates the ensemble pruning task as the two-objective optimization problem that comprises the empirical error and structural information among individual learners. We also provide a concrete implementation of MEPSI on decision trees. The theoretical results provide generalization bounds for both the general MEPSI approach and tree-based implementation. The comparative experiments conducted on multiple real-world data sets demonstrate the effectiveness of our proposed method.

Are serious games seriously good at preparing students for clinical practice?: A randomized controlled trial

Purpose Serious games (SGs) have great potential for pediatric medical education. This study evaluated the efficacy of a SG in improving learner satisfaction, knowledge, and behavior. Materials and methods This was an investigator-blinded randomized controlled trial (RCT) comparing a SG against two controls: (i) adaptive tutorial (AT), and (ii) low-stimulus control (LSC). SG is a highly immersive role-playing game in a virtual hospital. AT delivers interactive web-based lessons. LSC is paper-based clinical practice guidelines. Metropolitan senior medical students at UNSW were eligible. A total of 154 enrolled and were block randomized to one intervention. Participants had access to one intervention for 8 weeks which taught pediatric acute asthma and seizure assessment and management. Satisfaction was assessed with Likert-scale responses to 5 statements and 2 free-text comments. Knowledge was assessed with 10 multiple-choice questions (MCQs). Clinical behavior was assessed during a 30-point simulated clinical management scenario (CMS). Primary analysis was performed on a modified intention-to-treat basis and compared: (1) SG vs. AT; and (2) SG vs. LSC. Results A total of 118 participants were included in the primary analysis (modified intention-to-treat model). No significant differences in MCQ results between the SG and control groups. SG group outperformed the LSC group in the CMS, with a moderate effect (score out of 30: 20.8 (3.2) vs. 18.7 (3.2), respectively, d = 0.65 (0.2–1.1), p = 0.005). No statistically significant difference between SG and AT groups in the CMS (score: 20.8 (3.2) vs. 19.8 (3.1), respectively, d = 0.31 (–0.1 to 0.8), p = 0.18). A sensitivity analysis (per-protocol model) was performed with similar outcomes. Conclusions This is the first investigator-blinded RCT assessing the efficacy of a highly immersive SG on learner attitudes, knowledge acquisition, and performance in simulated pediatric clinical scenarios. The SG demonstrated improved translation of knowledge to a simulated clinical environment, particularly compared to LSC. SGs show promise in pediatric medical education.

Using Learning Analytics to Understand K–12 Learner Behavior in Online Video-Based Learning

This research investigated the potential of learning analytics (LA) as a tool for identifying and evaluating student behaviours associated with active learning within an online learning environment. The study focused on the application of LA for evaluating student engagement in video-based learning – an area of inquiry highlighted in literature as needing further investigation. Results determined that the LA method could identify active learning behaviours, and that LA can play a valuable role in providing information on learner activity in autonomous learning environments. However, LA did not provide a complete picture of learner behaviour and viewing strategies, highlighting the importance of a multi-method approach to research on online learner behaviours. It is anticipated the accessible approach outlined in this study will provide educators with a viable means of using LA techniques to better understand how learners interact with course content and learning objects, greatly assisting the design of online learning programmes.

Content integration of environmental management practices and responsible behavior for senior high school learners

Content integration of environmental management practices and responsible behavior for senior high school learners

Schools within the uMgungundlovu district: Principals’ perspectives on categorization and school functionality

School categorization has impaired the functionality of schools and led to dysfunctional teaching and learning processes. This study explores principals' perspectives on categorization and school functionality. The existing literature on school categorization focuses on the geographical areas of schools and resource allocation, ignoring the complexities of learner behavior and other dynamics within schools. Based on a qualitative case study within an interpretive paradigm, this study was conducted in the uMgungundlovu district of KwaZulu-Natal using semi-structured interviews, document reviews, and observations with five purposefully selected principals of five primary schools. Data analysis revealed that schools were paying significant maintenance and bills to sustain themselves without sufficient funding. In addition, the quintile classification indicates that the socioeconomic status of schools weakens the institution's ability to provide intensive development and growth for administrative and learning needs and to the extent that schools generate their resources for the reconstruction of structures and facilities, affecting the well-being of teachers and learners, as well as external factors such as professional development and academic training, which have a significant impact on the delivery of quality education.

Integrating Business Analytics in Educational Decision-Making: A Multifaceted Approach to Enhance Learning Outcomes in EFL Contexts

This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper delineates patterns in learner engagement, evaluates factors that affect these patterns, and examines the relationship between these factors and educational outcomes. The study provides an empirical analysis that elucidates the connection between learner behaviors and learning outcomes by employing machine learning, process mining, and statistical methods such as hierarchical clustering, process discovery, and the Mann–Kendall test. The analysis determines that learning patterns, characterized as single-phase or multi-phase, repetitive or non-repetitive, and sequential or self-regulated, are more closely associated with the nature of the educational content—such as books, series, or reading levels—than learner characteristics. Furthermore, it has been observed that learners exhibiting self-regulated learning patterns tend to achieve superior academic outcomes. The findings advocate for integrating analytics in educational practices, offer strategic insights for educational enhancements, and propose a new perspective on the connection between learner behavior and educational success.

Peace Studies, Knowledge and Learner Achievement

All conscientious school leaders as well as parents aspire for peaceful schools where knowledge about peace will thrive, and role-players will be conscious human rights. Research has demonstrated that when learners have learnt about peace they are able to reshape their thinking as they consciously build optimism about the world. Arguably, all countries would embrace schools that promote peace within the curriculum. Yet, apart from promoting reflection and dialogue on peace, few studies have examined the link between peace and learner achievement. As such this study examined the impact of peace studies on learner achievement. The qualitative study was conducted in four high schools that had been riddled by violence over a period of two years. Teachers in these schools had undergone informal peace training in attempts to lessen violence. The findings revealed that learners learned about handling conflicts, understanding the concept of good values as well as respect and knowledge. It was also found that learners whose schools have run critical programmes in peace studies are likely to use positive school climate, useful knowledge, and maximisation of positive learner behavior for their success. Peace may therefore be a little recognised factor that may support learner success and minimise the dropout rates. Finally, the study showed that peace and progress go hand in hand when teachers and their learners do not have to focus on addressing violence. The schools become more prosperous when they use stability to enhance a positive climate and promote peace and learners’ achievement.
 
 

Enterprise Implementation of Educational Technology: Exploring Employee Learning Behavior in E-Learning Environments

In the rapidly evolving landscape of information technology and with the ubiquitousness of the internet, corporations are increasingly focused on harnessing educational technology to boost their competitive prowess. A pivotal question emerges: Can they implement this technology effectively and sustainably to enhance the learning capabilities of their workforce and facilitate their accumulation of knowledge and skills? This concern remains a central focus in the corporate sphere. From educational psychology, goal orientation theory offers an explanatory framework for understanding learner (employee) behavior variations following learning interventions. This research is grounded in the e-learning environment fostered by educational technology within corporations. It explores and hypothesizes the impact of employee achievement motivations—including learning, proving, and avoiding goal orientations—on self-regulated learning (encompassing cognitive, motivational, and behavioral adjustments). Additionally, this study examines how employees’ self-regulated learning and learning satisfaction with the learning process influence learning effectiveness (learning outcomes) assessments. Our empirical survey targeted 380 employees from 26 companies participating in corporate educational technology learning (e-learning), with our research hypotheses tested through PLS structural equation modeling. The analysis indicates that employees’ learning and proving goal orientations indirectly positively affect their learning outcomes by mediating self-regulated learning and learning satisfaction. Conversely, employees’ avoidance goal orientation indirectly negatively impacts their learning outcomes by mediating their self-regulated learning and learning satisfaction. Finally, the researchers offer recommendations for management and future research directions.

Multi-knowledge enhanced graph convolution for learning resource recommendation

In recent years, E-learning has gained immense popularity as a prominent mode of education. However, accurately recommending learning resources from a vast amount of data remains a significant challenge. This study addresses two primary challenges impacting recommendation performance. Firstly, an imbalance exists between the abundance of available learning resources and the limited interaction behavior of learners. Secondly, the existing algorithms often overlook dynamic preference information, focusing primarily on learners’ short-term, static preferences only by learning interactive behavior but disregarding the multi-correlation between learning resources and learners. To tackle these challenges, we propose MkEGC (Multi-knowledge Enhanced Graph Convolution), a novel framework for learning resource recommendation. We approach the recommendation process as a Markov decision process. Initially, we construct a dual knowledge graph convolutional network, operating in learning resource-knowledge and learner-knowledge domains. This network facilitates the extraction of vector features from learning resources, enhances the learner vector representation, and captures higher-order preferences. Subsequently, we design hierarchical and attention weighting strategies to effectively extract latent hierarchical information from the knowledge graph. Finally, we integrate the learning resource state, the learning interaction state, and the sequence state to represent a multi-dimensional learner-state within the Markov decision framework, enabling prise learning resource recommendations. To validate the effectiveness of MkEGC, we conduct extensive experiments, comparing multiple sets of metrics with six state-of-the-art recommendation algorithms, utilizing two real-world datasets.

Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity.

Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students' experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, and policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors.

What the experts see: A qualitative analysis of the behaviors of master adaptive learners in emergency medicine.

The Master Adaptive Learner (MAL) model postulates that learners develop adaptive expertise through cycles of self-regulated learning. Despite a robust theoretical basis, the actual observable behaviors of MALs are not well characterized. We sought to define behaviors that characterize MALs within emergency medicine (EM) training. Using a constructivist grounded theory approach, we analyzed semistructured interviews with expert EM educators. These experts reflected on observable behaviors as well as factors in the clinical learning environment that may modulate these behaviors. We recruited using purposive sampling until thematic saturation. We identified four overall themes, of which three described groups of learner behaviors and a fourth described modifiers of these behaviors. Learner behaviors include: (1) critical interrogation of practice, (2) intellectual risk-taking, and (3) intentional curation of a learning network. Critical interrogation of practice encompasses several observable behaviors including learner-driven feedback conversations, independent synthesis of clinical information, appropriate deviation from algorithms based on their conceptual understanding of core principles, intentional use of case variation and hypothetical questioning, and continuous refinement of decisions. MALs also engage in intellectual risk-taking for their development by communicating clinical decision-making processes even at the risk of being wrong, openly addressing errors and gaps, and intentionally seeking out uncomfortable experiences. Intentional curation of a learning network is the deliberate development of a consortium of trusted individuals who serve as mentors and sounding boards. We also identified a fourth theme related to the expression of learner behaviors: learning environment modulates behaviors. Active promotion of psychological safety is necessary for learners to express these behaviors. This safety is mediated through trusting relationships and expert supervisors who serve as colearners and role models. We present several behaviors that allow identification of MALs among EM trainees. These data expand our understanding of MALs and the critical influence of the learning environment. Identification of these behaviors may allow for more precise categorization of targeted curricular interventions and meaningful learning outcomes.

Learning and unlearning voicing assimilation

This study investigates how postlexical phonological processes are acquired in multilingual speech, namely, how learners cope with conflicting demands in the production and perception of the voicing patterns in their non-native languages, what impact lexical knowledge has on learner behavior, and to what extent existing speech learning models can account for it. To investigate this, 14 Hungarian native speakers, proficient sequential learners of Spanish and English, took part in two types of experiment. The production experiments examined regressive voicing assimilation between obstruents and when the trigger was a sonorant consonant (presonorant voicing) word-internally and across word-boundary. At word level, we compared various lexical groups: non-cognates, double cognates and triple cognates (inhibitory, facilitative, and cognates with conflicting information). The perception experiments aimed to find out whether learners notice the voicing assimilations mentioned. The results showed that participants failed to learn presonorant voicing and failed to block regressive voicing assimilation despite perceiving the latter as linguistically relevant. Data also revealed that there is no direct link between perception and production, and that cognate status had a limited effect, but in triple cognates the primacy of the native language was dominant. Thus, it is concluded that in laryngeal postlexical processes the native language plays the primary role, neither the other non-native language, nor linguistic proximity seems to be decisive. Our data can be best accounted for by the Scalpel Model extended to phonological acquisition.

Towards prescriptive analytics of self‐regulated learning strategies: A reinforcement learning approach

AbstractSelf‐regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self‐regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real‐time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose‐built OLE to develop a long short‐term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualised SRL focused scaffolding. Practitioner notesWhat is already known about this topic Learners often fail to adequately adapt their behavior to the self‐regulatory demands of e‐Learning environments. In order to promote effective Self‐regulated learning (SRL) capabilities, researchers and educators need tools that are able to analyze and diagnose a learner's SRL strategy use. Current methods for SRL analysis are more often descriptive as opposed to prescriptive and have limited utility for real‐time analysis or support of a learner's SRL behavior. What this paper adds This paper proposes the use of Reinforcement Learning for prescriptive analytics of SRL. We train a Reinforcement Learning agent on sequences of SRL processes acted by learners in order to learn the optimal SRL strategy for a given learning task. Implications for practice and/or policy Our work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualized SRL focused scaffolding. The implications of our work can aid in course design by predicting the self‐regulatory load imposed by a given task. The ability to model SRL strategies using Reinforcement Learning can be extended to simulate or test SRL theories.

An evolving learning style detection approach for online education using bipartite graph embedding

The increasingly widespread online education recently enabled a new form of teaching, learning, and overall educational outcome. However, the need of personalization is required as learners learn differently and learning follows a one-size-fits-all approach. Learning style is the most used personalization that plays an important role in learning. This learning style is a changeable trait that is influenced by the learner's behavior based on past experiences. As a result, knowing it over time helps to point learners in the right direction, motivate them, and enhance their learning outcomes. Research done so far does not take into account the changes in the behavior of learners, and the behavioral data is at large volume, making the existing approaches fail to capture and extract the behavior of learners efficiently. Inspired by these constraints, we propose an incremental learning style detection approach for online education with a bipartite graph embedding technique. We first construct a dynamic bipartite graph to represent the incremental interaction between learners and learning resources while learning. Then, introduce a dynamic bipartite graph embedding to learn the low dimensional representation of the constructed graph from the current and previous time. Finally, the low dimensional features are mapped to the selected Felder-Silverman learning style model (FSLSM) dimension each time to identify and group similar learners using the k-means clustering algorithm. The proposed approach can be integrated into different educational systems. Extensive experiments conducted using three datasets from 2015 KDD Cup courses demonstrate the effectiveness of our approach. Average of 93.19%, 95.76%, and 98.48% are achieved across the three datasets in accuracy compared to the existing approaches.

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

In the context of Big data, large-scale open online courses increase learning paths for learners, but in the face of countless high-quality curriculum resources, it is easy for derivative learners to face the dilemma of rich curriculum resources but difficult to choose resources, which leads to information maze for learners. How to help learners quickly and accurately find their own learning resources in the explosive growth of MOOC resources is an urgent problem in the field of education Big data. However, the traditional Collaborative filtering recommendation technology does not perform well when dealing with sparse data and cold start. The recommendation content is repeated and can not effectively deal with high-dimensional and nonlinear data of online learning users, resulting in low efficiency of resource recommendation. Therefore, the study adopts a deep belief network (DBN) to construct a personalized resource recommendation model. The model combines the learner behavior characteristics with the curriculum resource content attribute characteristics to form the learner feature vector. The parameters of the model are adjusted according to the characteristics of learners. Through experiments, the proposed model has shown good performance. The experiment explored the effects of training set size, learner characteristics, and GPU on model performance. The experimental results show that when the training set proportion is 100%, the RMSE, Accuracy, Recall, and F1 values of the model are 0.76, 0.946, 0.957, and 0.951, respectively. When the model is trained using a training set containing learner features, the RMSE, Accuracy, Recall, and F1 values of the model are 0.75, 0.962, 0.908, and 0.958, respectively. After using GPU to accelerate the model, the running time of the model decreased from 360 minutes to 90 minutes. The results indicate that the model cannot effectively mine data information when the degree of correlation between sample information is low. The richer the relationships between samples, the better the performance of the model. Simultaneously learning hunger feature vectors and learner behavior feature vectors for training can significantly improve the recommendation accuracy of the model. The main contribution of this study is to propose a recommendation method based on DBN classification to replace traditional similarity calculation methods, using DBN's efficient feature abstraction and feature extraction capabilities to fully explore learners' interest and preference for course resources. In addition, in view of the common problems of cold start and data sparsity in traditional Collaborative filtering recommendation methods, the research deeply mines the characteristics of learners' Demographics and curriculum resources' content attributes, and constructs a learner interest model based on DBN combined with learners' behavior characteristics, which effectively solves the problems of cold start and data sparsity, as well as the inaccurate expression of learners' interest preferences for curriculum resources.

Construction and Algorithm Research of Online Learning Interaction Behavior Model Based on FBM

Abstract The development of online learning environments has facilitated changes in educational interactions. This study focuses on an in-depth exploration of learners’ online interaction behaviors based on the theory of the Fogg Behavior Model, all in the context of accurately depicting and optimizing these interactions. Considering the complexity and richness of online education platform data, this paper proposes an improved analytical methodology to accurately model and predict learner behaviors, expecting to provide a scientific basis for the development of personalized teaching strategies and the enhancement of learning experience This methodology is based on the Fractional Brownian Motion and BAS algorithms to analyze the data of online learning platforms. The study collected data such as login, learning status, and course grades, and used the combination algorithm to optimize the Hurst index estimation and improve prediction accuracy. The number of view notifications is significantly correlated with the interactive learning behavior of resources at the 0.01 level, and the correlation coefficients are 0.297, 0.557 and 0.360. The algorithm accurately reflects learner behavior, provides optimization strategies for online learning platforms, and enhances learning efficiency and interaction quality.

A Study on Influencing Factors of Dynamic Chinese Character Stroke Writing Behavior in CFL Beginners Using Digital Ink Technology

Abstract Exploring the intricacies of learning Chinese, this study leverages digital ink technology to observe the stroke writing behaviors of novice learners. We delved into the nuances of stroke order, direction, and the fluidity of writing, scrutinizing 2,131 Chinese characters to uncover the relationship between character structure and writing behavior. Our findings illuminate beginners’ challenges, offering a novel perspective on teaching Chinese characters that caters to the dynamic nature of writing and learning.

Navigating the Future Landscape of Gamified Education

Gamified education is undergoing a transformative shift, heralding a new era in the landscape of learning. This evolution is characterized by the advent of personalized learning experiences, underpinned by artificial intelligence (AI), and the seamless integration of virtual and augmented reality (VR/AR) technologies. These trends are not merely additive but are synergistically enhancing the learning experience, making it more dynamic, engaging, and effective than ever before. Personalized learning through AI is at the forefront of this transformation. AI algorithms are being leveraged to tailor educational content to the individual learner’s needs, preferences, and learning pace. This ensures that each student receives a customized learning experience that is optimized for their personal learning journey, maximizing engagement and efficacy. The integration of VR and AR technologies into gamified education is another significant trend. These technologies provide immersive learning environments that can simulate real-world scenarios or abstract concepts, thereby facilitating a deeper understanding and retention of knowledge. VR and AR make learning more interactive and enjoyable, which in turn, increases motivation and engagement among learners. Data-driven insights are playing a crucial role in the evolution of gamified education. By analyzing data on learner performance and behavior, educators can gain valuable insights into the effectiveness of teaching strategies and learning materials. This enables continuous improvement of the learning experience, ensuring that it remains relevant, engaging, and effective. Ethical considerations are paramount as gamified education continues to evolve. Issues such as privacy, data security, and equitable access to educational technologies are critical and must be addressed to ensure that gamified education benefits all learners, without compromising their rights or well-being. As technology, pedagogy, and game design converge, gamified education is evolving from a passive to an active, participatory model. This transformation empowers students to take charge of their educational journeys, offering a path to personalized, engaging, and data-driven learning experiences. The future of gamified education promises to be dynamic and inclusive, reshaping the future of learning in profound ways.