Learning Management Research Articles

ЄВРОПЕЇЗАЦІЯ ПРОФЕСІЙНОЇ ПІДГОТОВКИ МАЙБУТНЬОГО ФАХІВЦЯ ГОТЕЛЬНО-РЕСТОРАННОЇ СПРАВИ: МОЖЛИВОСТІ ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ

The purpose of the study is possibilities and prospects of using artificial intelligence for Europeanization of professional training of future specialists in hotel and restaurant business. To achieve this goal, we set several tasks: to study European standards of professional training of specialists; to evaluate current trends in application of artificial intelligence in education; to identify opportunities and challenges of AI integration into educational process of professional training of future specialists in hotel and restaurant business. During the scientific search, a complex of methods was applied: bibliographic, analysis and synthesis for the study of psychological and pedagogical literature (theoretical). A monographic method was used for logical presentation of the results. Results. Europeanization of professional training of future specialists in hotel and restaurant business of Ukraine as an important stage of adaptation of national education system to international standards has been studied. Main standards are considered through partial characterization of a number of documents. Challenges of Europeanization are characterized, namely: standards and accreditation, updating of curricula and programs, human resources, financing. Possibilities of Europeanization of professional training of future specialists in hotel and restaurant business, including improving quality of education, possibility of international internships and exchanges, increasing prestige of the profession of future specialists in the field of hotel and restaurant business and likely attraction of investments, have been allocated. Role of artificial intelligence in professional education has been investigated. Importance of AI has been proved through the implementation of a number of innovative solutions that can significantly improve educational process, namely: adaptive learning systems, virtual simulators, learning management systems. Conclusions. Main results of the study show that use of artificial intelligence has great potential for Europeanization of professional training in the field of hotel and restaurant business. Prospects for further research may include studying the impact of AI on specific aspects of educational process, developing new technologies for educational purposes, and analyzing long-term effects of AI integration on the labor market. Key words: professional training, future specialists, hotel and restaurant business, higher education institution, artificial intelligence, Europeanization.

The relationship between academic performance and the interaction patterns of online students in learning management system

The relationship between academic performance and the interaction patterns of online students in learning management system

The Role of Technology in Enhancing the Effectiveness of Integrated Performance Appraisal Frameworks

The role of technology in enhancing the effectiveness of integrated performance appraisal frameworks has become increasingly important in modern organizational management. As businesses strive for improved productivity, engagement, and employee satisfaction, traditional performance appraisal methods have shown limitations in providing real-time, accurate, and constructive feedback. This research explores how technological innovations such as artificial intelligence (AI), machine learning, and cloud-based human resource management software contribute to the enhancement of performance appraisal systems. By integrating these technologies, organizations can automate data collection, reduce bias, offer personalized feedback, and continuously monitor employee progress. This study investigates the impact of such technological integration on the accuracy, efficiency, and overall effectiveness of performance appraisals, ultimately linking these improvements to organizational performance, employee development, and retention. The findings aim to provide organizations with actionable insights into adopting and leveraging technology to create more robust, fair, and dynamic performance appraisal systems.

Machine Learning and Fog Computing-Enabled Sensor Drift Management in Precision Agriculture

Machine Learning and Fog Computing-Enabled Sensor Drift Management in Precision Agriculture

Innovative Technologies in Education for Socio-Economic Development in Africa

The continent of Africa faces numerous socio-economic challenges that impede her development. Despite the abundance of natural and mineral resources, Africa has over 2000 tertiary institutions, with Nigeria alone having over 200 universities, polytechnics, colleges of agriculture, and colleges of education. All these tertiary education institutes in Africa/Nigeria boast of having well over 4000 professors. In spite of all these educational establishments, only a few of these innovative technologies emanate from Africa. This paper presents a working definition of the term innovative technology and how it stands as a promising hope for addressing these challenges and fostering socio-economic development. Innovations such as e-learning platforms, learning management systems, mobile learning, virtual and augmented reality, open educational resources, blockchain for credentials, and renewable energy solutions offer potential solutions to improve educational access and quality. The successful implementation of these technologies can enhance educational outcomes, reduce poverty, and support sustainable development across the continent. This review also identifies the barriers to the full realization of these technologies, such as poor infrastructure, digital divides, and socio-economic disparities. It offers suggestions for policymakers, educators, and stakeholders on how to leverage technology for sustainable socio-economic growth. The ultimate goal is to foster an inclusive and equitable educational environment that empowers all African learners and contributes to the continent's overall development. Keywords: Innovative Technologies, Education, Socio-economic Development in Africa.

Educação Inteligente e Imersiva e Educação para o Desenvolvimento Sustentável

This article investigates the transformative potential of Smart and Immersive Education in promoting Education for Sustainable Development (ESD), connecting technological innovations to global sustainability demands. Through a conceptual model, it explores the contributions of emerging Technologies — such as Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI) — for the transformation of education, making it adaptive, engaging, and aligned with the Sustainable Development Goals (SDGs). The argument is made that these technologies enhance learning and empower both managers and students to take on leading roles in addressing socio-environmental challenges. The study provides a basis for new empirical research and educational practices, suggesting pathways to turn environmental and social challenges into opportunities for innovation and positive impact.

The impact of frequency and stakes of formative assessment on student achievement in higher education: A learning analytics study

AbstractBackgroundResearch shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress. Optional assessments may encourage voluntary engagement, potentially leading to a more genuine reflection of student understanding. Also, frequent assessments provide continuous opportunities for feedback and adjustment, which can keep students actively engaged in the learning process.ObjectivesThis study aims to investigate two crucial facets of formative assessments: frequency and the level of stakes involved (mandatory vs. optional). We examine how modifying the frequency of formative assessments affects students' course performance. Additionally, we evaluate the impact of mandatory versus optional formative assessments on students' course performance in higher education.MethodsThe sample of this study consisted of undergraduate students (n = 336) enrolled in three sections of a large asynchronous course at a Canadian university. We extracted features associated with online formative assessments (e.g., the number of attempts and average scores) from the learning management system. Next, we used these features to predict students' performance in summative assessments (two midterms and a final exam).Results and ConclusionsOur findings indicated that increasing the frequency of online formative assessments did not consistently improve student performance. Also, participation frequency in online formative assessments seemed to vary depending on assessment stakes (i.e., optional vs. mandatory). We recommend that instructors examine what conditions can maximize the contribution of formative assessments to students' academic achievement before building predictive models.

Proof of Concept of ChatGPT as a Virtual Tutor.

This study aimed to evaluate the feasibility and perceived value of an instructor-developed ChatGPT-based virtual tutor for third year PharmD students in a pharmacy management course. The objectives were to assess faculty time required to develop the tool and to analyze student engagement and perception of its educational value. The virtual tutor was developed by the instructor and consisted of custom ChatGPT prompts deployed via the course's learning management system. Development metrics were documented, including time invested and iterations needed for prompt refinement. A post-use questionnaire evaluated student uptake and experience, which included frequency of use, perceived value for learning and exam preparation, and overall satisfaction. The development of the virtual tutor required a total of 119minutes, demonstrating a feasible time investment. Ninety-two of 130 students completed the questionnaire for a 70% response rate. Sixty-one students (66%) engaged with the tutor, with varying degrees of use. Most students (82%) reported increased confidence for exams, and 99% acknowledged enhanced understanding of the course material through the tutor. The virtual tutor proved to be a feasible educational tool with considerable student uptake and satisfaction. It offered a time-efficient alternative for educational support, potentially reducing the need for traditional face-to-face tutoring. The study highlights the effectiveness and practicality of ChatGPT as a virtual tutor in pharmacy education, but further research should explore its application across different content areas as well as potential unintended consequences.

Performance of Basilan State College faculty: Metrics on commit, Knowledge, Teaching Prowess, and learning management

This study examined the teaching performance of faculty at Basilan State College, focusing on commitment, subject matter expertise, fostering independent learning, and managing the learning process. The study involved 147 faculty members, both permanent and non-permanent, during the 2008-2009 academic year, from four campuses: Isabela City, Sta. Clara, Lamitan, and Maluso. A stratified random sampling method was used to select 107 teachers, which included 44 permanent and 63 non-permanent staff. A survey was conducted to collect socio-demographic information such as age, gender, marital status, ethnicity, education, teaching experience, subject area, and employment type. Faculty performance in the specified areas was assessed using the NBC-461 Standard Performance Rating Form. Results indicated that faculty performance was rated as Outstanding by teachers, peers, and supervisors, while students rated it as Very Satisfactory. Notable differences were found in ratings from various evaluators, with teachers generally rating themselves higher than their peers, supervisors, and students. The study showed that performance levels remained consistent across age, gender, employment type, or subject area, but were significantly influenced by educational qualifications and teaching experience, with higher levels of both leading to better performance. These insights suggest that focused strategies at both the departmental and institutional levels could improve teaching effectiveness and support the achievement of educational objectives.

Evaluating the success of a learning management system for supplemental learning in accounting

ABSTRACT The purpose of this paper is to evaluate, from the student perspective, the critical factors that influence the success of a Learning Management System (LMS) used in a supplemental model in accounting education. Based on the Information System Success Model (ISSM) developed by Delone and McLean (2003) [The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Null, 19(4), 9–30], this research tests four hypothesised relationships around five key constructs: information content quality, instructor quality, self-regulation, perceived usefulness and satisfaction. To conduct this study, data from a sample of 313 cost accounting students in higher education were analysed, using the partial least squares method (PLS-SEM) with SmartPLS software. The findings show that student satisfaction levels with the LMS used in a supplemental model are explained by its perceived usefulness, to which the quality of the instructor and the quality of the information content are determinant factors. Contrary to expectations, high student self-regulation is found to weaken the positive relationship between perceived usefulness and satisfaction. This research expands the scarce literature on the adoption of LMS in accounting education and responds to calls to improve the effectiveness of accounting education and the efficiency of its delivery. Implications, limitations and future research lines are discussed.

Obstacles to the Use of AI Applications in Blackboard for Faculty Members at Northern Border University, Saudi Arabia

This study investigates the impediments and facilitators of artificial intelligence (AI) use in the Blackboard Learning Management System. A survey of 286 faculty members at Northern Border University examined demographics, AI tool usage frequency, adoption barriers, and factors influencing AI integration in teaching. Results revealed significant perceived barriers to AI tool use in Blackboard, with insufficient support and training identified as the primary obstacle. The survey found that gender and educational level influenced the adoption of the AI tool. The study suggests that addressing faculty training needs, ensuring privacy and security, and improving AI tool reliability could help overcome perceived impediments to AI use in Blackboard. These findings contribute to understanding the challenges and potential solutions for integrating AI in higher education learning management systems.

Learning Languages in the 21st Century: the Digital and Ai Transformation

The article deals with the problem of the learning languages in the 21st century: the digital and AI transformation. The aim of this article is to explore and clarify the transformative impact of digital technologies on foreign language teaching. This article explores the evolution of language learning from classroom-based methods to the incorporation of digital instruments, Artificial Intelligence (AI), and Information and Communication Technologies (ICT). Digital tools such as language learning applications, online dictionaries, multimedia resources, and language exchange platforms have made language acquisition more accessible and engaging. AI technologies introduce intelligent tutoring systems, personalized learning experiences, and advanced language processing capabilities, while ICT tools like Learning Management Systems (LMS) and virtual classrooms facilitate effective communication and information processing. The benefits of these advancements are multifaceted, including increased accessibility and flexibility, enhanced engagement and motivation, personalized learning paths, and improved collaboration and communication. However, challenges such as the digital divide, variability in resource quality, data privacy concerns, and the need for continuous teacher training must be addressed. Looking ahead, future trends such as augmented and virtual reality, blockchain technology, AI-driven content creation, IoT integration, and multimodal learning experiences promise to further transform the landscape of language education. These innovations will pave the way for more immersive and interconnected language learning experiences, contributing to a more multilingual global society.

Data-Driven Teamwork

Group assignments are a staple in higher education, providing opportunities to enhance collaboration and communication skills. However, we frequently encounter grievances related to group work as coordinators of large STEM units (800 students). Our student survey identifies work ethic and communication discrepancies between group members as barriers, often leading to uneven workloads, dissatisfaction, and diminished collaborative learning (Bacon et al., 1999). Common mitigation strategies to address work ethic discrepancies, like group charters and peer evaluations, often prove ineffective, since they could be ignored if not properly followed up or enforced (Chang & Brickman, 2018). Another barrier to group productivity lies in the team formation process itself. Traditional team formation methods – self-selection, random assignment, or instructor-led grouping – each present inherent limitation. Self-selection often results in homogenous groups susceptible to "groupthink" (Janis, 1982), limiting the diversity of thought essential for creative problem-solving. Random assignment, while seemingly impartial, can lead to imbalanced skillsets and an uneven distribution of workload (Bacon et al., 1999). While instructor-led grouping might seem ideal, it is logistically impractical within large cohorts (Decker, 1995). Computer-aided team selection offers a promising alternative. Its automated process streamlines team formation, generates diverse teams, and allows instructors to focus on teaching, despite concerns about increased workload (Cavanaugh et al., 2004). To address these group work challenges, we have developed a multi-pronged strategy integrating computer-aided team selection with a comprehensive follow-up and feedback framework. Our goal is to transform group work from a source of anxiety to a rewarding learning experience. Our approach combines technology with strategic interventions: Computer-aided Team Formation: We utilise Gruepr, an open-source team selection software (Hertz, 2021), to generate groups based on student preferences including work styles and meeting preferences (Cavanaugh et al., 2004). Gruepr integrates seamlessly with our learning management system, Canvas. Structured Communication, Collaboration, and Proactive Monitoring: Regular temperature-check surveys deployed through Canvas allow students to rate their group's progress and workload distribution. This data enables early identification of potential issues. To encourage group dialogue, the aggregated and anonymised outcome is shared with team members Facilitating Peer Feedback and Communication through SRES: The Student Relationship Engagement System (SRES), a learning analytics tool (Liu et al., 2017), was used to communicate aggregated peer feedback to individual group members, encouraging constructive dialogues regarding group progress. Our evaluation compared student performance, feedback, and complaints from the current year to previous years, alongside an estimation of staff workload. Preliminary findings demonstrate a reduction in staff time spent on group formation using Gruepr's automated grouping, highly contingent upon accurate survey design. Complaints, though still reported, were less severe, suggesting that regular temperature checks promoted proactive communication. Importantly, student feedback provided valuable insight, with many groups identifying the need for increased in-person interaction and improved scheduling strategies to accommodate diverse commitments. Our poster will elaborate on these results, showcasing both successes and challenges, and propose a refined workflow to further enhance collaborative learning experiences.

Data-Driven Teamwork

Group assignments are a staple in higher education, providing opportunities to enhance collaboration and communication skills. However, we frequently encounter grievances related to group work as coordinators of large STEM units (800 students). Our student survey identifies work ethic and communication discrepancies between group members as barriers, often leading to uneven workloads, dissatisfaction, and diminished collaborative learning (Bacon et al., 1999). Common mitigation strategies to address work ethic discrepancies, like group charters and peer evaluations, often prove ineffective, since they could be ignored if not properly followed up or enforced (Chang & Brickman, 2018). Another barrier to group productivity lies in the team formation process itself. Traditional team formation methods – self-selection, random assignment, or instructor-led grouping – each present inherent limitation. Self-selection often results in homogenous groups susceptible to "groupthink" (Janis, 1982), limiting the diversity of thought essential for creative problem-solving. Random assignment, while seemingly impartial, can lead to imbalanced skillsets and an uneven distribution of workload (Bacon et al., 1999). While instructor-led grouping might seem ideal, it is logistically impractical within large cohorts (Decker, 1995). Computer-aided team selection offers a promising alternative. Its automated process streamlines team formation, generates diverse teams, and allows instructors to focus on teaching, despite concerns about increased workload (Cavanaugh et al., 2004). To address these group work challenges, we have developed a multi-pronged strategy integrating computer-aided team selection with a comprehensive follow-up and feedback framework. Our goal is to transform group work from a source of anxiety to a rewarding learning experience. Our approach combines technology with strategic interventions: Computer-aided Team Formation: We utilise Gruepr, an open-source team selection software (Hertz, 2021), to generate groups based on student preferences including work styles and meeting preferences (Cavanaugh et al., 2004). Gruepr integrates seamlessly with our learning management system, Canvas. Structured Communication, Collaboration, and Proactive Monitoring: Regular temperature-check surveys deployed through Canvas allow students to rate their group's progress and workload distribution. This data enables early identification of potential issues. To encourage group dialogue, the aggregated and anonymised outcome is shared with team members Facilitating Peer Feedback and Communication through SRES: The Student Relationship Engagement System (SRES), a learning analytics tool (Liu et al., 2017), was used to communicate aggregated peer feedback to individual group members, encouraging constructive dialogues regarding group progress. Our evaluation compared student performance, feedback, and complaints from the current year to previous years, alongside an estimation of staff workload. Preliminary findings demonstrate a reduction in staff time spent on group formation using Gruepr's automated grouping, highly contingent upon accurate survey design. Complaints, though still reported, were less severe, suggesting that regular temperature checks promoted proactive communication. Importantly, student feedback provided valuable insight, with many groups identifying the need for increased in-person interaction and improved scheduling strategies to accommodate diverse commitments. Our presentation will elaborate on these results, showcasing both successes and challenges, and propose a refined workflow to further enhance collaborative learning experiences.

Enhancing feedback capabilities with the peer feedback module

Feedback has a powerful impact on student learning (Hattie & Timperley, 2007). Although it has now become an essential component of higher education courses, learning how to receive and provide feedback can be difficult, as evidenced by dissatisfaction in student surveys on a global scale (e.g., MacKay et al., 2019). Carless and Boud (2018) argue that for feedback to be effective, students need to develop their feedback-related capabilities. To enhance student feedback capabilities, feedback should be viewed as a complex process that has the potential to enhance learning and which emphasizes student agency (Niemienen et al., 2021). Peer feedback offers a range of opportunities for promoting student agency, where students practice constructing, delivering, and receiving feedback (Nicol et al., 2014). In the shift to hybrid and digital learning contexts, such opportunities are frequently mediated by technology, which may require different or additional capabilities to traditional teacher-led feedback practices. A small body of work from the peer feedback literature has focused on what specific capabilities students need during the feedback process. For example, Han and Xu (2020) argue that the literacy needed for peer feedback includes cognitive readiness (making judgements and taking action) and social-affective readiness (appreciating feedback and managing affect). To expand on this, peer feedback has been conceptualized as entailing a cognitive aspect (knowing what to critique in the work), a critical thinking aspect (such as judging work), and a socio-emotional aspect (considering emotions and the social climate) (Cheng et al., 2023). With the understanding that feedback literacy can be developed over time (Malecka et al., 2022) and a recent focus on developing interventions to improve feedback skills (see Little et al., 2024), this poster presentation details the creation and development of a feedback literacy intervention which contains an online module to develop peer feedback skills. To participate in the intervention, students are required to reflect on their feedback literacy levels using a recently developed feedback literacy behaviour scale (FLBS) (Dawson et al., 2023). Following this, participants engage in a student-led, self-paced online module nested within the University’s learning management system. Each sub-module targets different peer feedback capabilities which have been categorized into five dimensions, including domain knowledge, using evaluative judgement, crafting and delivering the feedback message, managing the socio-emotional environment, and using motivation. After participation in the online module, students complete an organic and pre-existing peer feedback activity before assessing their feedback literacy levels once more through the FLBS (Dawson et al., 2023). A smaller sample of students are involved in focus groups, where insights into how the module has the potential to influence feedback capabilities are discussed. Participants also bring along student work to highlight how their feedback capabilities have been used. Through this intervention, learners may develop better feedback practices (including general feedback literacy). Crucially, they may also learn how to provide effective and helpful feedback information to their peers, which is one of the key requirements for moving beyond university and into the workforce.

Effect of Gamification Combined with Mobile Learning on Chinese Language Speaking Skills of Second-Year Foreign Students at Guangdong University of Foreign Studies

Background and Aims: In the era of the mobile internet, transforming language learning content into interesting game tasks could stimulate students' interest in learning and improve engagement and learning outcomes. The study aimed to evaluate the impact of gamification combined with a mobile learning approach on students' Chinese language speaking skills and satisfaction. The significance of the study was to innovate teaching methods and improve oral expression. Students were guided to participate in interactive learning by designing gamified learning tasks with clear objectives. Methodology: This study utilized a pre-experimental design featuring a one-group pretest-posttest model. The sample of this study was 30 second-year foreign students studying at Guangdong University of Foreign Studies derived from a cluster random sampling method. The research tools were (1) Course lesson plan, (2) Chinese language speaking skill test, and (3) Student satisfaction questionnaire. Conduct pre-test and post-test on the sample using the Chinese speaking proficiency test, and analyze the mean, standard deviation, paired sample t-test, and one sample t-test of the data using statistics. Result: After using gamification combined with mobile learning, students' Chinese language speaking skills improved by 13.75%, surpassing the predetermined criteria of 70% and the difference was statistically significant at 0.05. Based on the scores from the statistical student satisfaction questionnaire, the overall results of Gamification combined with mobile learning by students are at a high level with (M=4.02, S.D. = 0.77) Conclusion: The study confirms that learning management using gamification combined with mobile learning significantly enhances Chinese language speaking skills it stimulates students' interest in learning, develops their independent learning ability, boosts their motivation and initiative, and caters to their individual learning needs. This is a new trend in future education.

Analysis of the Current Situation and Optimization Paths of Teaching Quality in Rural Primary Schools in Ganzi Prefecture under the Background of “Double Reduction”

To further implement the fundamental task of strengthening moral education and cultivating people, the country issued the “double reduction” policy in 2021. Nowadays, the whole country is reducing the academic burden on students with unprecedented policy intensity. Although the development speed of education in ethnic regions is relatively slow, this policy has also been gradually promoted and implemented in the basic education stage. This article takes “double reduction” as the background and observes the current situation of teaching quality in rural primary schools in Ganzi Prefecture through questionnaires and interviews. It analyzes the objective factors in three aspects: learning quality, teaching quality, and management quality, and proposes optimization paths for teaching quality.

Innovative Digital Learning

This poster highlights the effective use of learning technologies like animation, generative AI tools, and immersive reality to create digital learning and assessment artifacts for university nursing students. The purpose is to deliver a high-quality digital learning experience aligned with the university's pedagogy and education plan. It also showcases accessible, inclusive digital artifacts and early evaluations of their impact on learning. Animation technology is at the forefront of creating engaging educational content. By using animation, complex concepts can be shown in a visually appealing and easily digestible manner, making learning more engaging for students (Liu & Elms, 2019). It also allows for dynamic storytelling and the illustration of abstract ideas, which can be particularly beneficial in concepts that are challenging to visualize (Rasmusson & Bourne, 2017). Generative AI plays a pivotal role in developing scripts for these educational animations. Leveraging AI, we generate content tailored to learners' needs and optimized for clarity and engagement. Though AI-generated, our academic team thoroughly edits and revises the scripts to be clinically representative of the topic covered. This AI-driven script development ensures efficient collaboration with academics, resulting in accurate, relevant, and pedagogically sound material. (ProQuest LLC, 2024). Using cutting-edge animation and generative AI technologies can create impactful nursing education materials that boost student engagement. (White, Huang, & Campanale, 2023) and facilitate learning outcomes (Huang et al., 2020). These activities are anchored in the university’s signature pedagogy—active, applied, and authentic—immersing students in educational content and inspiring them to apply their knowledge and skills in real-world scenarios presented through animated learning activities. In addition, immersive reality technologies, including Virtual Reality (VR) and Augmented Reality (AR), further enhance the educational experience. By incorporating AR and VR as additional resources to complement the animations, we create immersive environments where students can interact with the material in a hands-on manner. This not only can make learning more engaging but can also help students better understand and retain complex concepts through experiential learning (Tene et al., 2024). A digital learning developer collaborated with the academic team to create an augmented reality-based artifact for nursing students. Integrated into Canvas our institution’s learning management system this artifact presented difficult topics in three dimensions with the aim of effectively explaining concepts and enhancing student engagement. While animation and immersive technologies can boost engagement, they can also present inclusivity and accessibility challenges for some students. To facilitateinclusivity and accessibility, we followed digital accessibility guidelines and universal design principles (Ismailov & Chiu, 2022). We evaluated tools like animation, AR/VR, and interactive modules for accessibility, offering alternatives when needed. This approach accommodated diverse learning preferences, making education accessible and inclusive for all students. The environmental impact of adopting these technologies is a significant consideration. Digital tools can reduce the need for physical materials and decrease travel-related emissions, making education more sustainable. The early assessment of the digital learning materials, incorporating animation, AI, and immersive learning technologies, reveals promising results in enhancing student engagement. These materials cater to various learning preferences reflecting insights from the academic team.

Assuring and Improving Learning Outcomes

Universities have a responsibility to measure and assure graduate learning outcomes ("Higher Education Standards Framework (Threshold Standards)," 2021; TEQSA, 2017). However, while assuring learning outcomes is a priority across the sector, many universities fail to collect meaningful data on student achievement of these outcomes (Martin & Mahat, 2017). Some institutions are investigating technologically enhanced processes to improve the efficiency and accuracy of learning-achievement data (Burrack & Musselwhite Thompson, 2021; El Marsafawy, 2022). Over the past four years, the University of Technology Sydney (UTS) Business School has made substantial enhancements to assurance of learning (AoL) practices, leveraging learning management system (LMS) functionality and data visualisation software to create a streamlined process, which better facilitates the continuous improvement of programs. This work is of particular interest to academic and professional staff involved in collecting and reporting on student achievement data, in program continuous improvement and evaluation, or in leveraging technology-enhanced learning analytics. The UTS Business School’s AACSB accreditation necessitates the collection, tracking, and reporting of student achievement of program-level learning outcomes (AACSB, 2020). Each program identifies subjects to "develop" and "assure" learning outcomes, with student achievement measured within specific assessment tasks and collated for program-level analysis. This process can be administratively complex, and historically involved multiple manually collated spreadsheets. This represented a substantial time cost for the faculty and often resulted in unreliable data. It was, for example, common practice to report overall subject marks rather than the specific learning outcome to determine achievement level, resulting in assessment practices that diverged from the recommended standards of constructive alignment (Biggs, 2014). In 2021, faculty T&L and central learning technologies teams commenced a pilot using LMS tools and the interactive data visualization tool PowerBI to streamline data collection, collation, and visualisation. Canvas Outcomes set up at the faculty level allows for centralised achievement-data collection, which, when linked to student and program information in PowerBI, can be used to obtain program-level insights. Beyond administrative efficiency, the project was also designed to improve and foreground the constructive alignment (Biggs, 2014) of measured learning outcomes. The revised process requires the embedding of learning-outcome-aligned assessment criteria within rubrics, resulting in improved constructive alignment and data transparency. The initial pilot was evaluated using Human Centred Design (IDEO.org, 2015) methods, which concluded the new system was an improvement on previous processes. Since the pilot, the faculty has completed a full rollout to over 60 subjects and 20 programs. Once this foundational AoL process was in place, program and subject-level continuous-improvement projects could be built upon it. Previous reporting methods had limited usefulness to Program Directors and were no longer fit for purpose due to changes in AACSB requirements. Reporting processes were therefore adjusted to focus on continuous improvement of key program metrics. In the new process, Program Directors and teaching teams review program data to identify curriculum or student-experience improvements that will enhance student achievement of learning outcomes. For example, a master's program identified that a high percentage of students were falling below expectations in two outcomes. Investigating the causes identified a need for substantial changes, with new subjects taking assurance responsibilities, and revisions to the program's overall assessment strategy. Recent data shows significant improvements in the target outcomes. This presentation will outline these updated technology-enhanced AoL processes demonstrating how the collected data can be used to improve university programs and the student learning experience.

Focusing the Juggernaut

Education technology (EdTech) is at the forefront of many universities' desires to improve, evolve and better the student experience. From learning management systems to hybrid capabilities, many processes are updated to ensure relevance in the face of challenges and modern trends (Josué et al., 2023). These updates can be small and incremental but can also come in the form of large-scale, institution-wide EdTech rollouts. Due to the scale and the diversity of our institutions, this is fraught with issues including aligning technology with pedagogy, ensuring digital literacy and training for educators (Bower, 2017), and infrastructure adequacy (Al-Busaidi and Al-Shihi, 2012). Universities invest heavily in resources to address challenges and promote sustained EdTech adoption (Deacon et al., 2022). Strategic and flexible educators, sometimes with differing perspectives from their institutions, can leverage these resources to achieve their individual educational objectives. This case study, situated at the University of New South Wales (UNSW), involved one such large scale EdTech adoption in the form of an institutionally wide digital assessment platform, InsperaTM, with a view to streamline and enhance the online assessment experience. This had a significant impact, reaching approximately 67 courses and 8.8k students in its first 3 terms of piloting, and to aid this implementation UNSW deployed multiple resources in the form of dedicated staff, in-depth training and central support for high stakes exams. With many educators already using other forms of digital assessment, the adoption was not uniformly accepted, creating friction and causing some to reject the platform. It also created opportunities for others. Wanting to uplift a newly inherited course, Dr Megan Kalucy saw InsperaTM, and the resources associated, as an ideal opportunity to improve student experience and reduce educator workload. With the support of the implementation team, Dr Kalucy adopted InsperaTM for specific in-course assessments, reaching approximately 300 students. Paper-based, short-answer, invigilated assessments, which were administratively inefficient and suffered from delays were replaced by weekly multiple-choice-question (MCQ) quizzes in InsperaTM. Automated marking, immediate feedback, and broader content coverage resulted in an overall improved experience for both staff and students, which was reflected in improved student feedback and reduced workload. This work investigates sector wide issues focusing on the research question of how educators can leverage collective EdTech resources to achieve individual goals. It addresses two key challenges; achieving widespread institutional adoption of EdTech and helping educators with increasing workloads find the time and resources to update their courses. A mixed-methods approach was used, employing a case study methodology comparing student satisfaction data, before and after, to validate the improvements made to the course through the adoption of Inspera. Qualitative data from staff and students was used to inform, implement and revise these improvements. The study found that technology adoption in the educational landscape is not exclusively dependent on knowledge of advancements, but more significantly about leveraging the resources available (Oyetade et al., 2020). Here, the universities adoption of Inspera employed strategies such as training, hands-on support, as well as platform and student support, lowering the energy barrier for improvement to Education Focused staff. These resources were deployed practically and with a focus on educators, increasing the number of early adopters and the uptake of Inspera across the university. This case study also shows that the diverse needs of various disciplines need to be considered for uniform EdTech adoption, as a ‘one size fits all’ approach is often ineffective.