Learning Behaviour Research Articles

Cognitive psychology and the classroom: what teachers need to know

This paper examines the dynamics of learning and how they relate to the optimal teaching strategies for learners. The authors aim to enhance learners' information processing abilities by highlighting the importance of considering different learning theories in the classroom. These considerations are based on the qualitative behaviour of learners' conscious awareness, cognitive and metacognitive engagement. A multi-stage cluster sampling procedure was employed to select a sample of 840 Grade 11 learners from 20 schools in the education district. A questionnaire was used to collect data, and statistical tests were conducted to validate and analyse the data. The study's results suggest that it is crucial for teachers to use a variety of learning theories and to consider variables that facilitate effective information processing by learners. Understanding diverse learning theories and their interplay positively influence the cognitive development of learners. The study's findings, which include SEM and HLM analyses and implications from teachers' application of juxtaposed theories, suggest that inefficiencies may exist in certain teaching approaches and in individual learners' information processing. Teachers who understand and apply the simultaneous impact of juxtaposed theories of learning can enable learners to assimilate or accommodate incoming information into their cognitive schema, leading to deeper and more meaningful understanding.

Vicarious learning and graduates' adaptive career behaviours: the case of graduates on clinical internships

PurposeThe effect of vicarious learning during clinical or medical internships on graduates' adaptive career behaviours has attracted scant attention from healthcare researchers, particularly, in the developing world context. Drawing upon the social cognitive career theory model of career self-management (SCCT-CSM), the current study examines how vicarious learning influences the clinical graduates' adaptive career behaviours (i.e. career exploration and decision-making) via career exploration and decision-making self-efficacy (CEDSE) and career intention.Design/methodology/approachData were collected from 293 nursing graduates undertaking clinical internships in 25 hospitals across Nigeria who willingly participated in this study as they were also assured of confidentiality at two-waves. The proposed hypotheses were tested using a path analysis.FindingsThe findings showed that vicarious learning during clinical internship had a direct effect on career exploration, decision-making and career decision self-efficacy among graduate trainees. Also, the findings revealed that the effects of vicarious learning on the graduates' career exploration and career decision-making were significantly mediated by career decision self-efficacy and career intentions.Practical implicationsThe findings of this study have important practical implications for higher education institutions and industries that send and receive clinical graduates for clinical internships to gain more skills. More emphasis should be on encouraging learners to learn vicariously in addition to other forms of learning experiences available during clinical internships.Originality/valueThe study explains that the graduates' higher engagement in clinical career exploration and decision-making was based on a higher level of vicarious learning during internships. The results suggest that higher education institutions and healthcare service providers can derive greater benefits from more emphasis on promoting vicarious learning during clinical internships.

The Mediating Role of Learning Analytics

Researchers in learning analytics have created indicators with learners’ trace data as a proxy for studying learner behaviour in a college course. Student Approaches to Learning (SAL) is one of the theories used to explain these behaviours, distinguishing between deep, surface, and organized study. In Latin America, researchers have demonstrated that organized approaches to learning could be more effective in higher education, leading to better performance and course approval. However, further analysis of student behavioural data is needed to understand this relationship and inform interventions targeting study habits and academic performance. In this study, we analyzed the relationship between student approaches to learning and their final grade in six college courses, using behavioural trace data as a mediator variable. Specifically, we conducted a quantitative study in two Latin American institutions where data of different granularity was collected from their Learning Management Systems. We observed that most learning analytics indicators do not mediate the effect between approaches to learning and course performance. However, there was evidence for fine-grained indicators acting as total mediators. Implications are discussed at methodological and pedagogical levels, aiming to inform the advancement of learning analytics in the region and its use for supporting student learning.

Uncovering Engagement Profiles of Young Learners in K–8 Education through Learning Analytics

E-learning platforms have become increasingly popular in K–8 education to promote student learning and enhance classroom teaching. Student interactions with these platforms produce trace data, which are digital records of learning processes. Although trace data have been effective in identifying learners’ engagement profiles in higher education and lifelong learning (e.g., MOOCs), similar research on young learners has been very scarce. This study makes a timely and novel contribution to the field by identifying emerging profiles of young students from Grades 1 to 8 based on their engagement in an e-learning platform. The k-means cluster analysis yielded seven distinct profiles in total. While the dominance of profiles differed across grade levels, some profiles were not even present at specific grades, indicating the changing engagement behaviours of young learners in different years. In addition, all emerging profiles suggest students tended to focus on specific components of the platform, thus lacking balanced engagement with all resources. In particular, while primary school students showed relatively higher interest in game-based learning, middle school students focused more on exams than their counterparts. The findings yielded seven specific considerations for the design of effective e-learning platforms.

A genetic algorithm for the Resource-Constrained Project Scheduling Problem with Alternative Subgraphs using a boolean satisfiability solver

This study evaluates a new solution approach for the Resource-Constrained Project Scheduling with Alternative Subgraphs (RCPSP-AS) in case that complex relations (i.e. nested and linked alternatives) are considered. In the RCPSP-AS, the project activity structure is extended with alternative activity sequences. This implies that only a subset of all activities should be scheduled, which corresponds with a set of activities in the project network that model an alternative execution mode for a work package. Since only the selected activities should be scheduled, the RCPSP-AS comes down to a traditional RCPSP problem when the selection subproblem is solved. It is known that the RCPSP and, hence, its extension to the RCPSP-AS is NP-hard. Since similar scheduling and selection subproblems have already been successfully solved by satisfiability (SAT) solvers in the existing literature, we aim to test the performance of a GA-SAT approach that is derived from the literature and adjusted to be able to deal with the problem-specific constraints of the RCPSP-AS. Computational results on small- and large-scale instances (both artificial and empirical) show that the algorithm can compete with existing metaheuristic algorithms from the literature. Also, the performance is compared with an exact mathematical solver and learning behaviour is observed and analysed. This research again validates the broad applicability of SAT solvers as well as the need to search for better and more suited algorithms for the RCPSP-AS and its extensions.

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.

Mapping gender networks of music self-concept and music emotions: A network analysis study of music majors in China.

Students' music self-concept and music emotions are becoming prominent topics within the area of music education. The majority of previous research on self-concept and music emotions has examined the two constructs independently and focused on gender differences in externalizing behaviours in music learning, but has neglected the internal interactions between individual music self-concept and music emotions. Network analysis is a promising method for visually examining music self-concept and music emotions as part of a network of interactions to identify core features and interrelationships among nodes in the network. In this study, 515 students majoring in music from a Chinese university were recruited. The results showed that high music self-concept and boredom were the common features at the core of the network for both men and women college students. The boredom exhibited by women differed from that of men in that men's boredom was directed at the entire music course, while boredom in women manifested as daydreaming and boredom with learning materials. This study is the first to explore gender differences in the music self-concept and music emotions from a holistic perspective. The findings could help music teachers gain insight into the complex system of music self-concept and music emotions. Music teachers could capture the respective features of men and women to design individualized teaching strategies.

Exploring Education for Children with 22q11.2 Deletion Syndrome: A Qualitative Study of Mothers Perspectives

22q11.2 deletion syndrome (22q11.2DS) is a complex and widely variable genetic syndrome involving multisystem physical health problems, significant cognitive and psychosocial challenges, all of which may impact upon learning and academic achievement. In the current study, we explored mothers’ perceptions of their child’s learning within early and primary educational contexts. We conducted 3 online focus groups and 1 online semi-structured interview with a total of 9 mothers of children diagnosed with 22q11.2DS. Mothers were included if their child had a genetic diagnosis of 22q11.2DS and was aged from 3 to 12 years old. The mothers were asked a series of open-ended questions to explore the educational experiences of their child. We adopted an inductive approach to our data analysis and conducted a reflexive thematic analysis resulting in the emergence of 4 major themes (1.) The impact of 22q11.2DS on a child’s learning; (2.) The impact of adaptive behaviour skills on their child’s learning; (3.) The level of support available within the educational setting and; (4.) Mothers’ perceptions about their child’s learning. From these themes, the following 12 sub themes were identified: absences affecting learning; the development of specific learning behaviours; the impact of a 22q11.2 DS diagnosis on their child’s self-perception; toileting, motor issues; communication; sleep; school and teacher support and the presence (or absence) of therapists. Finally, the mothers’ confidence about their child’s learning; mothers’ concerns and comparisons of their child; and mothers’ apprehension about their child’s future were all identified as sub-themes. Children with 22q11.2DS require increased and specific psychosocial support to allow them greater opportunities to fully participate in their learning environments. Mothers perceived the level of support their child received was greater during pre-school compared to that received at primary school. It was clear that a greater understanding of the needs of children with 22q11.2DS, by educators, was needed. Implications for tailored support for school-based learning and educational transitions for young children with 22q11.2DS are discussed.

How do teachers in vocational and higher education nudge their students? A qualitative study

ABSTRACT Nudging is a strategy from behavioural economics that subtly influences behaviour by changing the environment. Despite its recent introduction in education, no overview exists of what behavioural interventions are already practiced in education, and how these fit within nudging frameworks. In this qualitative study, we investigated what nudges are used in vocational and higher education by interviewing 33 teachers and 15 students, and conducting 11 classroom observations. We provide an overview of currently used nudges to promote learning behaviour, and distinguish between intuitive, didactic, and planned nudges. Results also show that teachers use nudging intuitively and based on didactic techniques, but are generally unaware of doing so. This provides opportunity for introducing explicit knowledge of nudging to augment teachers’ toolboxes for designing learning environments. Future research can test nudges teachers already use in practice, and further integrate the behavioural and educational perspectives on nudging interventions in an educational context.

The mediating role of intention of learning behaviour in learning behaviour.

Improving the quality of postgraduate study is one that must be addressed with the increase in the number of postgraduate students. This study aims to analyse the effects of learning attitude, learning motivation and self-efficacy on learning behaviour through the intention of learning behaviour, and the effect of learning behaviour on learning outcome. Measurements were made on 560 postgraduate students after the development of a scale. The scale was analysed for reliability and exploratory factor analysis using SPSS software. The date were then analysed using structural equation modelling (SEM) analysis techniques with path analysis and bootstrap methods. The results of the study showed that students' attitudes towards learning and self-efficacy had a significant indirect on learning behaviour through the mediating involvement of intention to learn behaviours, and learning behaviour had a significant effect on learning outcome. Therefore, there is a need to improve learning behaviour by improving students' intention to learn behaviour so that they can have good learning outcome.

The Challenges Entry-Level Mathematics Teachers Face in Conducting Blended Teaching

In recent years, there has been an increasing amount of literature on online teaching and learning. Teaching mathematics in the fourth industrial revolution offers entry-level mathematics teachers formidable challenges. Despite using various teaching aids in explaining the learning area, entry-level mathematics teachers, those with zero to three years of teaching experience, have experienced challenges in blended teaching during the COVID-19 pandemic. Such challenges could support the preconceived notion identified by others that the field of learning (mathematics) is difficult. This paper hinged on constructivist epistemology and investigated the challenges that entry-level mathematics teachers face in conducting blended teaching in the Sedibeng West District of Gauteng Province in the Republic of South Africa. Eight entry-level mathematics teachers were purposely sampled from four selected schools that participated in the article. This article adopted a case study design and responses were analysed thematically. The focus group interviews were used as tools to collect data in this study. The study revealed a variety of perceptions that entry-level mathematics teachers shared about the impact of blended teaching in mathematics learning. These included, but were not limited to, the use of teaching programs or software, the effects of load shedding and blended teaching challenges related to learners' performance and behaviour. The results of this study could provide program developers, subject advisors, school principals with other members of the management team, and mathematics teachers to support the entry-level mathematics teacher's confidence, sense of future and communication skills, as well as foster multigenerational connections in blended teaching.

Digital, Self-Regulated Vocabulary Learning and Device Control In Out-Of-Class, Higher Education Settings

Self-regulation of learning behaviour is particularly important when it comes to vocabulary learning for academic purposes in a second language because it often needs to be done on a regular and consistent basis and mostly in out-of-class, self-directed settings to be successful. Self-regulation is also vital when this learning takes place using digital activities on smartphones because these are now ubiquitous devices and deeply embedded in both daily life and higher education settings. Features such as notifications from social media applications can end up distracting students from their academic tasks unless they have the capacity to manage and control their behaviour. This naturalistic, mixed methods study conducted with students on an academic English foundation course in a higher education context aimed to measure their capacity for self-regulated vocabulary learning through technology before and after 10 weeks of intentional digital vocabulary learning in out-of-class settings and to see if there was any difference between learning on a laptop and a smartphone. The purpose of this study was to find out if device control was a relevant dimension of self-regulation, which is an under-researched area. The study collected quantitative data through a recently developed self-report survey tool, and differences in scores were measured using Wilcoxon signed-rank tests. Qualitative data was also collected from students through paired-depth interviews, and this was analysed using typological analysis. The results revealed that the students’ self-reported capacity for self-regulated vocabulary learning through laptops was significantly higher than their capacity for self-regulated vocabulary learning through smartphones. In addition, commitment regulation when using a smartphone decreased significantly over the 10-week period primarily due to distractions from social media notifications. At the same time, students were aware of when to use each device for different types of learning activities and under different temporal and spatial conditions. Overall, this study showed that device control should be considered an additional dimension of a model of digital, self-regulated vocabulary learning and should also be incorporated into future research in the field of e-learning. In addition, students in higher education need to be given more guidance about the benefits and drawbacks of different devices and how to develop their capacity and strategies for greater device self-regulation.

Students’ mathematics self-efficacy: a scoping review

Students’ mathematics self-efficacy (MSE) is strongly associated with learning behaviours and performance, and students’ future career choices. In our scoping review, we screened what substantive foci (conceptualization, directionality and role of MSE, change in MSE, and situational specificity of MSE) have been posed and which methodological approaches (participants, analytical methods, data sources, and congruence of measures) have been used in recent (2018–2022) studies of MSE. Studies of MSE were clearly in the mathematics domain with 21 of 49 included studies exploring specific mathematics areas. The key focus was on strength of MSE. International databases (i.e., PISA) have enabled broad generalization, while in-depth qualitative studies enable minute situation-specificity. Cross-sectional and longitudinal studies using hierarchically nested designs (i.e., students in classrooms) enable us to draw conclusions at both individual, classroom, and school levels. The current state-of-the-art indicates methodological advancement is rapid and we are likely to see further methodological-substantive synergies in the field of MSE in future studies. We see the potential and need for future mixed-methods studies that continue the focus on MSE as a multidimensional and dynamic concept. Careful consideration of the theoretical background of the construct of MSE continues to be important to bring the field forward.

Creating opportunities in a challenging environment: experiential crisis learning behaviour of tourism SMEs in Sri Lanka

Creating opportunities in a challenging environment: experiential crisis learning behaviour of tourism SMEs in Sri Lanka

The Development of an Instructional Model Based on Interactive Learning Theory to Improve Undergraduate Students’ Reflection Ability

This research aimed to: 1) study the factors that affect the development of the third-year students’ reflection ability in Baise University; 2) develop an instructional model based on interactive learning theory; 3) compare the third-year students’ reflection ability before and after using the instructional model based on interactive learning theory. The sample group was 32 third-year students’ reflection ability at Baise University. The research instruments were 1) questionnaires, 2) interview forms, 3) lesson plans, 4) questionnaire on reflection ability, and 5) teaching opinion interview form 6) observational records of student behaviour. The research was conducted in three steps: studying the factors that affect the development of third-year students’ reflection ability, developing an instructional model based on interactive learning theory, and the experimental and improvement process. The study results showed: 1) the factors that affect the development of students' reflection ability consisted of three aspects (1) school teaching management, (2) teachers' teaching style, and (3) students' learning behaviour 2) The instructional model based on interactive learning theory included four elements: (1) principle, (2) objectives, (3) learning process, and (4) results; 3) students’ reflection ability was improved after the implementation of an instructional model.

Exploring the Moderating Roles of Emotions, Attitudes, Environment, and Teachers in the Impact of Motivation on Learning Behaviours in Students' English Learning.

For several decades, extensive research has been conducted on motivation in language learning. However, how motivation impacts learning behaviours with the moderation of factors related to emotions, attitude, environment, and teachers has not been reported. This study aims to examine the moderating effects of these four motivational factors to explain the inconsistent effects of motivation on English learning behaviours across studies. Drawing on self-determination theory, the study investigated 182 high school English learners and explored how the four motivational factors moderate the relationship between students' motivation and their English learning behaviours. We first examined how the four motivational factors predicted intrinsic/extrinsic motivation and how intrinsic/extrinsic motivation predicted English learning behaviours. The results reveal that the four motivational factors all positively predicted intrinsic motivation, while language attitude positively predicted extrinsic motivation. Both intrinsic and extrinsic motivations positively predicted English learning behaviours, with intrinsic motivation exerting a stronger influence. Language attitude did not moderate the relationship between motivation and English learning behaviours. However, the other three factors enhanced the positive relationship between motivation and learning behaviours. Notably, at the low level of teacher-related factors, the impact of extrinsic motivation on English learning behaviours was insignificant. Related implications are discussed.

Collaborative Learning Management System with Analytical Insights: A Preliminary Study

The mode of teaching and learning had been drastically changed over the decades. Therefore, one approach might not fit into all scenarios. Collaborative learning promotes collaboration between the students in completing given tasks with common goals. In this paper, problem statements were formed: (i) the collaboration between students and their teachers in the virtual learning environment has been at the bare minimum, (ii) the learning management system implemented has not been fully utilised with the data and information collected academically. Moreover, systematic literature review (SLR) is practised to investigate insights about collaborative learning, learning management system (LMS) and analytical approaches for student profiling. The aim of this paper is to address three research questions formed in the SLR: (i) What is the most commonly practised methodology for collaborative learning? (ii) What are the typical practised analytical methods and models for student profiling? and (iii) What factors influence students to use the learning management system? Besides, collaborative learning enables the students to conduct group discussions and assignments, promoting mutual interactions and creating knowledge amongst them. Additionally, the third-party LMS lacks synchronous chat feature. A student's profile grants educators valuable insights into the student's academic performance and learning progress. This information contributes to predicting the student’s performance with the assistance of analytical approaches applied. The applied analytical approaches provide useful information about the student’s learning behaviour, allowing the teachers to take adequate action. As a result, a conceptual framework is constructed with hypotheses formulated, reflecting the relations between each construct. Besides, a dedicated collaborative learning management system with machine learning capabilities is an ideal solution, tackling students’ collaboration among peers and between teachers with their academic performance and behaviour taken into account.

Pre‐class learning analytics in flipped classroom: Focusing on resource management strategy, procrastination and repetitive learning

AbstractBackgroundDespite the many advantages of flipped learning, it is challenging for educators to ensure that students complete the pre‐class learning assignments before the in‐class session.ObjectivesUsing a learning analytics approach, this study analysed students' pre‐class video‐watching behaviour in flipped learning with a focus on learners' resource management strategies, procrastination, and repeated video‐watching.MethodsFor the empirical study, three types of data (i.e., log data, survey and exam) were obtained from 70 undergraduate students who were enrolled in pre‐service teacher program designed in a flipped classroom including pre‐class videos and in‐class session.Results and conclusionsThe findings showed significant differences between the non‐procrastination group and procrastination group in their video watching time and resource management strategies. Those who watched the pre‐class videos at home/dorm greatly outnumbered those who did not, and they performed better than students who watched the pre‐class videos in other locations.What are the major takeaways from the study?This finding highlights the importance of resource management strategies in terms of the time and study environment to promote success in flipped learning courses. However, the number of times students accessed the pre‐class videos or watched the videos before class was not correlated with learning achievement. The reason for re‐watching pre‐class videos is a suggested future direction so instructors can design effective flipped learning courses.

Exploratory analysis on learning behaviours that favour job crafting

PurposeThe purpose of this research is to determine key behaviours to be efficient in identifying and developing employees' talent. The article aims to address the relationship between learning agility and job crafting, the influence between them, and how this relationship is built to improve performance and adaptability. For this purpose, the research has analysed which behaviours obtain the highest scores in both scales (job crafting and learning agility), designing the tool which allows Human Resources (HR) professionals an efficient identification and development behaviours to get the versatile talent that companies and professionals of the future need.Design/methodology/approachUsing the questionnaire that has integrated the learning agility scale and the Spanish job crafting scale. Data were collected from a sample of business professionals in Spain. Factor analysis and hierarchical cluster analysis were used, using a classificatory variable with the 126 valid responses obtained.FindingsIn an ever-changing environment, continuous employee adaptation to his/her role within a company is a critical factor for its survival. However, there is a paucity of large-scale empirical research on which behaviours employees have to develop to increase their adaptative skills. Drawing on the outcome of extant literature, the authors identify learning agility as the construct that firms have to encourage in their employees to impact job crafting. The contribution of the paper is twofold: (1) the authors empirically explored the association and the effects of learning agility and its factor on the development of job crafting. Results demonstrated the association between the two constructs; further, higher scores in both learning agility and job crafting predict increased employability, and higher scores in job crafting are associated with higher scores in change agility; (2) this study provides a multidimensional instrument that provides HR departments with the key behaviours to recruit in order to develop talent to prepare employees to face future challenges, ensuring the right performance and sustainable impact in the environment.Research limitations/implicationsA limitation of this study is that it is done exclusively within Spanish companies, even though from different industries and with different characteristics. Therefore, future research is necessary and should be conducted in other countries in similar industries to explore the empirical findings from this study in additional contexts.Practical implicationsThis research has found a tool that might allow HR departments to measure what level of job crafting and learning agility their employees have and to identify what key behaviours they need to focus on in the recruitment or in their internal strategic HR action plan to overcome any future challenges in their organization.Social implicationsIn a scenario where artificial intelligence is modifying the professional landscape, generating uncertainty about which skills are best to develop, the results are a guide for enterprises as to where to focus plans for learning and training, as well as for business schools regarding the content provided in training programs.Originality/valueThe authors advance the literature by providing a theoretical base for understanding the relationship between job crafting and learning agility. This article offers some practical managerial recommendations that help the human resources department focus on behaviours that allow talent to be identified and recruited to ensure an effective organization.

Handling Massive Sparse Data in Recommendation Systems

Collaborative filtering-based recommendation systems have become significant in various domains due to their ability to provide personalised recommendations. In e-commerce, these systems analyse the browsing history and purchase patterns of users to recommend items. In the entertainment industry, collaborative filtering helps platforms like Netflix and Spotify recommend movies, shows and songs based on users’ past preferences and ratings. This technology also finds significance in online education, where it assists in suggesting relevant courses and learning materials based on a user’s interests and previous learning behaviour. Even though much research has been done in this domain, the problems of sparsity and scalability in collaborative filtering still exist. Data sparsity refers to too few preferences of users on items, and hence it would be difficult to understand users’ preferences. Recommendation systems must keep users engaged with fast responses, and hence there is a challenge in handling large data as these days it is growing quickly. Sparsity affects the recommendation accuracy, while scalability influences the complexity of processing the recommendations. The motivation behind the paper is to design an efficient algorithm to address the sparsity and scalability problems, which in turn provide a better user experience and increased user satisfaction. This paper proposes two separate, novel approaches that deal with both problems. In the first approach, an improved autoencoder is used to address sparsity, and later, its outcome is processed in a parallel and distributed manner using a MapReduce-based [Formula: see text]-means clustering algorithm with the Elbow method. Since the [Formula: see text]-means clustering technique uses a predetermined number of clusters, it may not improve accuracy. So, the elbow method identifies the optimal number of clusters for the [Formula: see text]-means algorithm. In the second approach, a MapReduce-based Gaussian Mixture Model (GMM) with Expectation-Maximization (EM) is proposed to handle large volumes of sparse data. Both the proposed algorithms are implemented using MovieLens 20M and Netflix movie recommendation datasets to generate movie recommendations and are compared with the other state-of-the-art approaches. For comparison, metrics like RMSE, MAE, precision, recall, and F-score are used. The outcomes demonstrate that the second proposed strategy outperformed state-of-the-art approaches.