Learning Pathways Research Articles

Dusting for fingerprints: Tracking online student engagement

Hybrid learning strategies blend face-to-face instruction with online components, using Learning Management Systems (LMSs) as key platforms for educational resources. In strategies like the flipped classroom, students need to follow a specific learning pathway to complete certain activities in the LMS before class. This includes watching videos and completing readings and quizzes, to prepare for hands-on exercises during classroom time. Consistent student engagement with this approach is vital for success – but in large subjects with hundreds of enrolments, monitoring that engagement is a complex task. Using the data collected in an LMS, this paper presents an approach for detecting significant changes in student engagement in hybrid learning environments. The approach uses Process Mining (PM), a family of tools and techniques to analyze data through a process lens, to compare students' learning pathways between pairs of learning windows (e.g., weeks in a semester). Using a real-life event log containing more than 26,000 interactions of 194 students over a full semester, the findings demonstrate the approach's ability to detect changes in student engagement over time. These insights can be used by educators to refine their instructional design, deliver targeted interventions, and ultimately improve the overall effectiveness of hybrid learning.

Religious Knowledge Production and Digital Affordances

Abstract: This essay revisits a research trend that examines the impact of digital media on religious authority, concentrating on a specific genre of authority related to religious knowledge production, specifically “embodied knowledge” within Muslim intellectual history, pedagogical practices, and epistemology. Fundamental to the discussion is the potential decline of embodied knowledge, which is traditionally acquired through direct interaction with a living scholar or spiritual master. The essay emphasizes the increasing prevalence of disembodied knowledge facilitated by digital media’s expanding affordances. This shift signifies a transition in knowledge transmission from direct engagement with scholars to digital interfaces. With the rapid advancement of artificial intelligence within the broader knowledge economy, including the religious sector, a growing number of students of religion are turning to digital platforms for their learning. Concomitantly, Muslim scholars, educators, and preachers, some of whom may have unverified credentials, are leveraging these technologies to attract students and, at times, monetize their online popularity. This migration from embodied to disembodied pathways of learning and teaching has garnered significant attention from scholars researching this phenomenon.

Farmer experiences with goat raising in Lao PDR: implications for improving husbandry and sustaining viable systems

ABSTRACT Goat raising has increased in Lao People’s Democratic Republic due to meat demand from Vietnam. Goat production is low input using free grazing and minimal husbandry. Technical constraints are well known but socio-cultural contexts are poorly understood. This paper describes qualitative research on farmer’s motivations, experiences and learning pathways, to improve goat husbandry. Semi-structured interviews were conducted with 30 smallholder goat farmers in Lao’s South-Central province of Savannakhet. The interviews revealed that farmers relied on their experiences and observations of goat raising in their communities to make their management decisions. Trial and error was a valued learning strategy. Farmers took guidance from the exchange of goat raising experiences with other goat farmers and preferred participatory learning that fostered discussion in familiar village settings. Lao smallholder goat farming systems are a product of compromise across a number of resources. Project interventions that required low investments of labour, land or capital were most readily implemented and adapted by farmers. This study questions the assumption that project interventions are inherently optimal and beneficial for smallholder farming systems. Recommendations propose how farmer’s experiences, constraints and preferences can be incorporated into development approaches to ensure management system changes reflect the desires of smallholder farmers.

Revolutionizing Sports Education: Harnessing Innovations and Technology for Enhanced Learning

This paper explores cutting-edge innovations in sports education, focusing on active learning strategies, the flipped classroom model, gamification, differentiated instruction, and the integration of technology. Active learning methodologies, such as problem-based learning and cooperative learning, engage students in critical thinking and collaborative problem-solving, while the flipped classroom model prioritizes experiential learning and learner autonomy. Gamification principles add elements of competition and rewards to enhance motivation and engagement, while differentiated instruction tailors learning experiences to individual student needs. Furthermore, technology integration, including virtual reality simulations, wearable technology, and online learning platforms, revolutionizes sports education by providing immersive experiences, real-time performance monitoring, and personalized learning pathways. Through a comprehensive review and analysis, this paper elucidates the transformative potential of these innovations in optimizing student learning outcomes and preparing athletes for success both on and off the field.

Enhancing language acquisition through personalized learning: The role of collaborative filtering and recommender systems in TESOL

This paper explores the transformative potential of Collaborative Filtering (CF) and Recommender Systems (RS) in Teaching English to Speakers of Other Languages (TESOL). By leveraging data-driven insights from learner interactions, these technologies offer personalized learning experiences that significantly enhance language acquisition, engagement, and retention. Through empirical evidence and quantitative analyses, we demonstrate the positive impact of CF and RS on learners' proficiency, vocabulary acquisition, and communicative competence. The integration of CF and RS into TESOL not only facilitates adaptive learning pathways but also addresses practical implementation challenges, including privacy, ethical concerns, and technological barriers. This study underscores the efficacy of personalized learning recommendations in creating more engaging, efficient, and effective language learning environments.

Digital Computing-Based Course Recommendation Algorithm for Distance Education Platforms

A course recommendation algorithm utilizes data about a user's preferences, past behaviour, and possibly other factors like demographics or interests to suggest relevant courses. It employs techniques such as collaborative filtering, content-based filtering, or hybrid approaches to analyse similarities between users or courses and make personalized recommendations. By continuously refining its suggestions based on user feedback and interactions, the algorithm aims to enhance the user's learning experience by presenting courses that align with their interests and goals. This paper explores the integration of course design principles with recommendation systems to enhance personalized learning experiences in distance education platforms. The course design is performed with the integration of collaborative filtering with the edge computing model for the estimation of features in distance education. Collaborative filtering is applied in the education platform through the estimation of features and edge computing is implemented for the processing. With the increasing popularity of online learning, there is a growing need to tailor educational content to meet the diverse needs, preferences, and skill levels of individual learners. Course design plays a crucial role in shaping the structure and delivery of educational materials, while recommendation systems leverage user data to provide personalized course suggestions. By integrating these two components, distance education platforms can create tailored learning pathways that optimize user engagement, retention, and learning outcomes. The analysis is further enriched by showcasing the course recommendations for individual users, highlighting how recommendation systems leverage course design aspects to deliver personalized learning experiences.

Analysis and Modelling Of English Learning Behaviour Based on Data Mining Technology

This paper presents an in-depth exploration into the analysis and modelling of English learning behaviour leveraging data mining technology. In today's digital age, understanding the intricacies of how individuals acquire a second language is paramount for optimizing educational strategies and enhancing learning outcomes. By employing advanced data mining techniques, we aim to uncover patterns, trends, and underlying dynamics within English learning datasets, shedding light on the diverse behaviours exhibited by learners. Furthermore, this research transcends analysis, as we endeavour to construct robust models capable of simulating and predicting English learning trajectories. By leveraging insights derived from extensive data exploration, we aim to develop predictive models that offer personalized recommendations and adaptive learning pathways tailored to individual learner profiles. Ultimately, this paper contributes to the ongoing discourse surrounding language acquisition by providing valuable insights into English learning behaviours. By harnessing the power of data mining technology, we aspire to empower educators, policymakers, and digital learning platforms with the tools necessary to cultivate more efficient and inclusive approaches to English language education.

Design and Application of Intelligent Assisted Learning System for Higher Education Students

The Intelligent Assisted Learning System for Higher Education Students is an innovative educational platform designed to enhance the learning experience for college and university students. Leveraging artificial intelligence, machine learning, and data analytics, this system provides personalized learning pathways, adaptive tutoring, and real-time feedback to students based on their learning styles, preferences, and performance. By analyzing student interactions, engagement levels, and academic progress, the system dynamically adjusts course materials, assignments, and assessments to optimize learning outcomes. This paper explores the integration of Cooperative User Knowledge Modelling (CUKN) into Intelligent Assisted Learning Systems (IALS) tailored for higher education. Through a systematic investigation of CUKN's role within IALS, we elucidate its potential to enhance personalized learning experiences for students. By leveraging user data, collaborative filtering techniques, and adaptive learning strategies, CUKN empowers IALS to dynamically tailor content recommendations, learning paths, and collaborative tools to individual student needs, preferences, and proficiency levels. Simulation results suggest significant improvements in student engagement and performance, with metrics such as a 30% increase in learning efficacy and an 85% satisfaction rate with feedback mechanisms. While promising, challenges remain in refining algorithms and ensuring seamless integration into educational practices.

REVIEW OF WORKFORCE UPSKILLING INITIATIVES FOR EMERGING TECHNOLOGIES IN IT

This study provides a comprehensive analysis of workforce upskilling initiatives for emerging technologies in the Information Technology (IT) sector, with the primary objective of identifying effective strategies to prepare professionals for the rapidly evolving digital landscape. Employing a systematic literature review and content analysis, the research scrutinizes various upskilling programs, focusing on their design, implementation, and outcomes. Data sources include peer-reviewed journals and conference proceedings, selected based on criteria that ensure relevance and timeliness to the current IT context. Key findings reveal the critical need for anticipating technological trends to align workforce capabilities with future demands, highlighting the significance of personalized and experiential learning pathways. The study emphasizes the strategic role of upskilling in bridging the digital divide, advocating for inclusive and accessible learning opportunities to cultivate a diverse and globally competitive workforce. Recommendations for stakeholders center around adopting flexible learning models, fostering industry-academia partnerships, and emphasizing the development of both technical and soft skills. Finally, the study underscores the imperative for a proactive and collaborative approach to IT education and workforce development. By integrating advanced learning technologies and inclusive strategies, stakeholders can ensure the IT workforce remains agile and prepared to contribute innovatively in the face of technological advancements. This framework sets the foundation for future research and policy-making, aiming to enhance the resilience and diversity of the IT sector globally.
 Keywords: Workforce Upskilling, Emerging Technologies, IT Education, Inclusive Learning Strategies.

Clinical tutoring for nursing students: analysis of the Barletta-Andria-Trani Health Facility experience

BACKGROUND: University nursing tutoring from clinical nurses is an essential and necessary component in supporting students to be successful in curricular activities in support of classroom learning. The aim of this study was to describe the experience of the nurse involved in clinical tutoring of students in the Bachelor of Science in Nursing course at an Italian university. METHODS: A survey questionnaire created on the basis of a literature review was sent to all nurses involved in clinical tutoring within the ASL Bt. RESULTS: 67.44% reported that they had never received any kind of specific training despite the fact that 92.51% considered it essential for the correct performance of the role. 82.13% believe that this activity is not adequately valued CONCLUSIONS: The results obtained represent beginning of an educational pathway aimed at standardising the training processes of the clinical tutor, oriented towards improving the learning pathway of the future nursing class.

ML-CPC: A Pathway for Machine Learning Based Campus Placement Classification

The intense competition among students for a limited number of job opportunities poses a significant challenge to campus placements. There are various strategies that organizations can employ to tackle this issue. Primarily, it is essential to provide high-quality educational programs and opportunities for professional development that align with current market needs. This involves regularly updating the curriculum, integrating sector-relevant projects, and facilitating hands-on training experiences. Campus placements play a crucial role in evaluating an institution's caliber and ensuring the employability of its students. Institutions can enhance their placement records by implementing innovative solutions to challenges encountered in placements, such as intense competition and economic fluctuations. This requires a proactive strategy, collaboration with businesses, a focus on skill enhancement, and support for students' soft skills and professional development. By implementing these corrective measures, institutions can contribute to students' future success by better preparing them for the workforce. The primary objective of this paper is to conduct an exploratory analysis of the recruitment dataset. The application of supervised machine learning is employed to predict whether a student was placed, utilizing classification models. The proposed approaches and methods surpass all other machine learning models, achieving a recall value of 1, accuracy of 0.9524, precision of 0.8667, and an F1 score value of 0.9286 to address the ensemble architectural.

Intelligent Construction of University Music Education Teaching System Based on Artificial Intelligence Technology

The university music education teaching system plays a vital role in nurturing aspiring musicians and music enthusiasts by providing a comprehensive framework for musical learning and development. This system typically encompasses a diverse range of courses, workshops, and performance opportunities designed to cultivate students' musical talents, theoretical knowledge, and practical skills. Through a combination of classroom instruction, ensemble rehearsals, private lessons, and hands-on experiences, students receive a well-rounded musical education that covers various genres, styles, and traditions. Moreover, university music education teaching systems often incorporate state-of-the-art facilities, including rehearsal rooms, recording studios, and performance venues, to support students' artistic growth and creative expression. This paper presents an innovative approach to the intelligent construction of university music education teaching systems, leveraging artificial intelligence (AI) technology with Intelligent Fuzzy Regression Classification (IFRC). Recognizing the complexity and diversity of music education, this research aims to optimize teaching methodologies and enhance learning outcomes through AI-driven strategies. The proposed approach integrates AI technology, particularly IFRC, into the design and implementation of university music education teaching systems. IFRC combines fuzzy logic with regression analysis and classification techniques to model and predict complex relationships within music education datasets, enabling the system to adapt and respond dynamically to student needs and preferences. The IFRC-enhanced teaching system can generate personalized learning pathways, recommend tailored resources, and provide real-time feedback to students and instructors alike. This intelligent adaptation to individual learning styles and progress fosters a more engaging, effective, and inclusive music education environment. The IFRC-enhanced teaching system achieves an average improvement of 25% in student performance compared to traditional teaching methods. Moreover, specific musical skills, such as sight-reading, ear training, and music theory comprehension, exhibit notable enhancements, with average score increases of 30%, 20%, and 35%, respectively.

Application of Frbf Network Autonomous Learning Algorithm Based on Intrinsic Motivation in English Network Teaching

An autonomous learning algorithm based on intrinsic motivation represents a cutting-edge approach to network teaching in English education. By harnessing intrinsic motivation, this algorithm empowers learners to autonomously engage with English language materials and activities, driven by their innate curiosity and desire for mastery. Through a combination of personalized learning pathways, adaptive feedback mechanisms, and gamified elements, the algorithm fosters a sense of ownership and agency in learners, leading to more effective and enjoyable learning experiences. Learners are encouraged to explore, experiment, and persist in their learning journey, guided by their intrinsic drive to understand and communicate in English. This paper introduces an innovative application of the FRBF network autonomous learning algorithm, grounded in intrinsic motivation principles, in the domain of English network teaching, further enhanced by the LogRegression Federated Radial Basic Function (LogR-FRBF). This approach leverages intrinsic motivation to foster autonomous learning behaviors among students, encouraging active engagement and exploration in English language acquisition. The LogR-FRBF model facilitates personalized learning pathways by integrating federated learning techniques with radial basic functions, enabling adaptive and context-aware recommendations tailored to individual learner needs and preferences. Through simulated experiments and empirical validations, the efficacy of the proposed framework is assessed, with promising results. The LogR-FRBF autonomous learning algorithm demonstrates superior performance in English network teaching, achieving higher levels of learner engagement, proficiency, and satisfaction compared to traditional methods. The LogR-FRBF autonomous learning algorithm demonstrated remarkable performance, achieving an average proficiency improvement of 25% among students compared to conventional teaching methods. Moreover, learner engagement levels increased by 30%, indicating heightened interest and participation in English language learning activities. Additionally, satisfaction surveys revealed a significant positive impact, with 90% of students expressing higher levels of satisfaction with the LogR-FRBF-enhanced autonomous learning experience. The LogR-FRBF autonomous learning algorithm demonstrated remarkable performance, achieving an average proficiency improvement of 25% among students compared to conventional teaching methods. Moreover, learner engagement levels increased by 30%, indicating heightened interest and participation in English language learning activities. Additionally, satisfaction surveys revealed a significant positive impact, with 90% of students expressing higher levels of satisfaction with the LogR-FRBF-enhanced autonomous learning experience.

Integrating Neural Pathways for Learning in Deep Reinforcement Learning Models

Considering that the human brain is the most powerful, generalizable, and energy-efficient computer we know of, it makes the most sense to look to neuroscience for ideas regarding deep learning model improvements. I propose one such idea, augmenting a traditional Advantage-Actor-Critic (A2C) model with additional learning signals akin to those in the brain. Pursuing this direction of research should hopefully result in a new reinforcement learning (RL) control paradigm that can learn from fewer examples, train with greater stability, and possibly consume less energy.

Patient Learning Pathway: Identifying Patient Competencies in Teledermatology for Effective Management of Dermatological Conditions.

Dermatology consultations in Québec, Canada, face accessibility challenges, with most dermatologists concentrated in urban areas. Teledermatology, offering remote diagnosis and treatment, holds promise in overcoming these limitations. However, concerns regarding patient-doctor relationships and logistical issues exist. This article aims to introduce a dermatology patient learning pathway (PLP) developed by the Centre of Excellence on Partnership with Patients and the Public (CEPPP), focusing on knowledge, abilities, and skills mobilized by patients and their loved ones at key moments of the life course with an illness, as well as emerging educational needs. The PLP development was co-developed with dermatology patient and caregiver partners, stakeholders, and the CEPPP team. The process encompassed stakeholder engagement, exploration, recruitment of patient and caregiver partners, co-development of the PLP draft, and validation through consensus building. The PLP methodology led to the creation of 44 learning objectives, comprising a total of 107 subobjectives. These objectives were organized into 8 phases of the patient life course with a dermatological condition: (1) prevention and predisposition; (2) discovery, self-examination, or observation of a change; (3) first consultation; (4) wandering; (5) consultation with a dermatologist; (6) diagnosis; (7) treatments; and (8) living with it. The dermatology PLP serves as a resource outlining patient competency across different stages of managing a dermatological condition throughout their life course. In the context of teledermatology, the PLP might facilitate patient and caregiver engagement by helping select appropriate information and tools to support active participation in care.

There is No End to Learning, but How Does it Begin? A Meta‐Analysis of the Team Learning Pathway

AbstractWe complete a meta‐analytic investigation across two phases to increase understanding about team learning, an important process that is a challenge for teams to enact. In the first phase, we create a meta‐analytic database of 198 independent samples (N = 15,536 teams) to summarize the strength of the relationships between learning and team antecedents and outcomes. Motivational emergent states (e.g., potency) exhibited the strongest relationship with learning, followed by affective (e.g., psychological safety) and cognitive (e.g., trust) emergent states. Our results also highlight the positive relationship between learning and various antecedents, including team structure, supportive organizational context, environmental uncertainty, cognitive diversity, and gender diversity. In the second phase, we create an additional meta‐analytic database with 53 independent samples (N = 4,468 teams) to test a serial mediation model, demonstrating that psychological safety and learning serially mediate the relationships between team learning orientation and salient team outcomes (i.e., performance and innovation).

Coach to learn and learn to coach: synergising performance and development in the athlete-coach-environment learning system

ABSTRACT While high-performance sport traditionally highlights a dualist perspective on distinct pathways of development and performance coaching, an ecological dynamics rationale recognises the deeply entwined relations between development and performance. Acknowledging an athlete-environment-centred approach, an increasingly relevant topic concerns “coach learning”, supporting the idea that theories of athlete development present useful insights for understanding coach development. In this position paper, it is argued that athlete and coach learning are not independent from one another, thus forming part of an athlete-coach-environment learning system. This first of two insights papers discusses the contiguity between athlete development and performance and coach learning. It seeks to highlight a dual coach learning pathway towards “coaching to learn” (infused by knowledge of the environment) and “learning to coach” (supported by knowledge about the environment). To underline the interconnectedness of athlete/coach learning, two examples are discussed from: I) the 2021 Wheelchair Rugby League World Cup; and II), high-performance workshops for Olympic sport coaches.

The Chronic Effects of a Single Low-Intensity Blast Exposure on Phosphoproteome Networks and Cognitive Function Influenced by Mutant Tau Overexpression.

Blast-induced neurotrauma (BINT) is a pressing concern for veterans and civilians exposed to explosive devices. Affected personnel may have increased risk for long-term cognitive decline and developing tauopathies including Alzheimer's disease-related disorders (ADRD) or frontal-temporal dementia (FTD). The goal of this study was to identify the effect of BINT on molecular networks and their modulation by mutant tau in transgenic (Tg) mice overexpressing the human tau P301L mutation (rTg4510) linked to FTD or non-carriers. The primary focus was on the phosphoproteome because of the prominent role of hyperphosphorylation in neurological disorders. Discrimination learning was assessed following injury in the subsequent 6 weeks, using the automated home-cage monitoring CognitionWall platform. At 40 days post injury, label-free phosphoproteomics was used to evaluate molecular networks in the frontal cortex of mice. Utilizing a weighted peptide co-expression network analysis (WpCNA) approach, we identified phosphopeptide networks tied to associative learning and mossy-fiber pathways and those which predicted learning outcomes. Phosphorylation levels in these networks were inversely related to learning and linked to synaptic dysfunction, cognitive decline, and dementia including Atp6v1a and Itsn1. Low-intensity blast (LIB) selectively increased pSer262tau in rTg4510, a site implicated in initiating tauopathy. Additionally, individual and group level analyses identified the Arhgap33 phosphopeptide as an indicator of BINT-induced cognitive impairment predominantly in rTg4510 mice. This study unveils novel interactions between ADRD genetic susceptibility, BINT, and cognitive decline, thus identifying dysregulated pathways as targets in potential precision-medicine focused therapeutics to alleviate the disease burden among those affected by BINT.

Adaptive Learning Systems: Exploring Personalized Paths in Vocational Education

This research endeavors to delve into the application of adaptive learning systems within the context of vocational education, with the aim of elevating students academic performance and engagement. Adaptive learning systems represent an educational technology innovation that harnesses the power of artificial intelligence and machine learning to furnish students with personalized learning pathways and real-time feedback. The study encompasses the design and implementation of the system, methods for data collection and processing, as well as data analysis techniques. Through a diverse array of data analyses, we scrutinize the impact of adaptive learning systems on students' academic achievements and levels of engagement. The research outcomes elucidate the latent affirmative effects of adaptive learning systems in vocational education, endowing students with a more flexible and personalized learning experience.

Enhancing English Competence Based on TOEFL Result with the Use of the Expert System with Forward Chaining Method

This article delves into a progressive approach for enhancing English competence based on TOEFL results through the utilization of an expert system employing the forward chaining method. As a benchmark for English proficiency, TOEFL necessitates a strategic and personalized approach to address individual weaknesses. The proposed expert system analyzes TOEFL performance, identifies specific areas of improvement, and tailors learning pathways accordingly. By employing forward chaining, the system can effectively guide learners through a logical progression of targeted exercises, tailored study plans, and recommended resources to fortify identified language skill deficits. This article sheds light on how this innovative integration of technology and pedagogy can significantly bolster English language competence for TOEFL success and the overall English language proficiency.
 Keywords: expert system, forward Chaining, TOEFL