Refining the Learning Analytics Capability Model: A Single Case Study

Learning analytics can help higher educational institutions improve learning. Its adoption, however, is a complex undertaking. The Learning Analytics Capability Model describes what 34 organizational capabilities must be developed to support the successful adoption of learning analytics. This paper described the first iteration to evaluate and refine the current, theoretical model. During a case study, we conducted four semi-structured interviews and collected (internal) documentation at a Dutch university that is mature in the use of student data to improve learning. Based on the empirical data, we merged seven capabilities, renamed three capabilities, and improved the definitions of all others. Six capabilities absent in extant learning analytics models are present at the case organization, implying that they are important to learning analytics adoption. As a result, the new, refined Learning Analytics Capability Model comprises 31 capabilities. Finally, some challenges were identified, showing that even mature organizations still have issues to overcome

The Effectiveness of Multimedia and Virtual Environments in Light of the Conflict Strategy to Reduce Misconceptions in Mathematics among First-Year University Students

The roots of misconceptions in mathematics among first-year students at Hashemite University can be traced back to a flaw in the foundation of their previous mathematics knowledge acquired over the course of their academic years. This flaw presents a significant challenge for them during their studies. This study aimed to determine the effectiveness of multimedia and virtual environments in reducing misconceptions in mathematics among first-year students at Hashemite University, based on the conflict strategy. The study sample consisted of a random cluster sample of 109 male and female students from Hashemite University. The researcher observed the students and recorded their test scores. The researcher conducted face-to-face interviews to collect data for the study and performed statistical analysis using the t-test. The results showed that the percentage of misconceptions in mathematics among firstyear students at the Hashemite University for types 1, 2, 3, 4, and 5, respectively, amounted to 23%, 8%, 13.8%, 14.7%, and 0%. And 13.8%. The study examined the effectiveness of multimedia and virtual environments in reducing misconceptions in mathematics among first-year students at Hashemite University, based on the conflict strategy

Improving Students Performance in Small-Scale Online Courses -- A Machine Learning-Based Intervention

The birth of massive open online courses (MOOCs) has had an undeniable effect on how teaching is being delivered. It seems that traditional in class teaching is becoming less popular with the young generation, the generation that wants to choose when, where and at what pace they are learning. As such, many universities are moving towards taking their courses, at least partially, online. However, online courses, although very appealing to the younger generation of learners, come at a cost. For example, the dropout rate of such courses is higher than that of more traditional ones, and the reduced in person interaction with the teachers results in less timely guidance and intervention from the educators. Machine learning (ML) based approaches have shown phenomenal successes in other domains. The existing stigma that applying ML based techniques requires a large amount of data seems to be a bottleneck when dealing with small scale courses with limited amounts of produced data. In this study, we show not only that the data collected from an online learning management system could be well utilized in order to predict students overall performance but also that it could be used to propose timely intervention strategies to boost the students performance level. The results of this study indicate that effective intervention strategies could be suggested as early as the middle of the course to change the course of students progress for the better. We also present an assistive pedagogical tool based on the outcome of this study, to assist in identifying challenging students and in suggesting early intervention strategies

Putting learning back into learning analytics: actions for policy makers, researchers, and practitioners

This paper is based on (a) a literature review focussing on the impact of learning analytics on supporting learning and teaching, (b) a Delphi study involving international expert discussion on current opportunities and challenges of learning analytics as well as (c) outlining a research agenda for closing identified research gaps. Issues and challenges facing educators linked to learning analytics and current research gaps were organised into four themes, the further development of which by the expert panel, led to six strategy and action areas. The four themes are 1. development of data literacy in all stakeholders, 2. updating of guiding principles and policies of educational data, 3. standards needed for ethical practices with data quality assurance, and 4. flexible user-centred design for a variety of users of analytics, starting with learners and ensuring that learners and learning is not harmed. The strategies and actions are outcomes of the expert panel discussion and are offered as provocations to organise and focus the researcher, policymaker and practitioner dialogs needed to make progress in the field

Pengaruh Self Regulated Learning dan Minat Belajar terhadap Hasil Belajar PKK di SMKN 10 Surabaya

Hasil belajar yang baik bukan semata-mata diperoleh karena faktor kecerdasan siswa, tetapi faktor lain pun ikut mempengaruhi, antara lain cara siswa mengontrol belajarnya sendiri dan minat belajar yang baik. Penelitian ini dilakukan guna mengetahui pengaruh self regulated learning dan minat belajar secara simultan dan parsial terhadap hasil belajar PKK di SMKN 10 Surabaya. Jenis metode penelitian yang digunakan adalah penelitian kuantitatif. Intrumen dalam penelitian ini berupa kuesioner, wawancara, dan dokumentasi. 144 siswa kelas XI di SMKN 10 Surabaya merupakan populasi dalam penelitian ini sedangkan jumlah sampel penelitian 60 dari populasi tersebut. Metode analisis yang digunakan yaitu analisis regresi linier berganda. Hasil dari penelitian ini : 1). Self regulated learning berpengaruh positif dan signifikan terhadap hasil belajar produk kreatif kewirausahaan (PKK), pengaruh yang diberikan sebesar 12%, 2). Minat belajar berpengaruh positif dan signifikan terhadap hasil belajar produk kreatif kewirausahaan (PKK), pengaruh yang diberikan sebesar 16,6%, 3). Self regulated learning dan minat belajar secara simultan berpengaruh positif dan signifikan terhadap hasil belajar produk kreatif kewirausahaan (PKK) di SMKN 10 Surabaya, dan pengaruh yang diberikan sebesar 28,6 %

Twenty-five years study (1995–2019) of Food and Bioproducts Processing: An overview of research trends

In the current study, we presented an overview of the publication profile of Food and Bioproducts Processing (FBP), a leading international journal on food processing. The detailed analysis was made to measure its scientific progress from 1995 to 2019 by identifying publication trends, most cited articles, leading institutes, and profolic countries. The publication dataset and citations information were retrieved from the Scopus bibliographic database hosted by Elsevier. Several scientific achievements were observed in publications (n=1548), impact factor 3.726 or CiteScore 6.10, and the citations (a total of 33,663) over the 25-year time frame. The factorial analysis revealed that the journal research focuses on two clusters. The first cluster focused on moisture determination, spray drying, mathematical models, thermal processing foods, food products and food processing, and the second cluster focuses on research areas of the dimension of surface properties, organic solvents, response surface methodology, antioxidant activities, flavonoids, solvent extraction and fermentation. Although citations have increased significantly need wider publicity of the work. The most cited articles were identified with the interdisciplinary research within food science and technology and added to reinforce science advancement within the field. Overall, these findings highlighted the evolution, progress, quality, and efficiency of the journal and provided early-profession researchers/specialists with an opportunity to lead more inventive studies in food science and technology (FST)

The Challenge of Artificial Intelligence

Artificial Intelligence (AI) appears to be advancing at an ever-accelerating pace and affecting much of human life. The power of AI has already been demonstrated in various areas – from smartphone personal assistants and customer support chatbots to medical diagnoses and driverless cars. At the same time, these applications bring multiple challenges and much hyperbole. Nonetheless, of particular importance here, AI systems have also entered the classroom. However, while promising to enhance education, the design and deployment of these tools again raise particular concerns and challenges. We begin this chapter with a brief history and definition of AI outlining the evolution of AI techniques aiming to imitate or outperform human cognitive capacities. We continue by exploring what AI systems promise to deliver in educational contexts and their impact on learners, examining the interaction through the lens of three analytical categories: learning with AI, learning about AI and preparing for AI. We also explore the risks related to the introduction of AI into education and investigate transversal issues related to all three categories, noting that currently little attention has been paid to what is ethically acceptable for AI and education. Finally, we conclude by trying to answer two questions: how can we make better AI tools for education and how can education help address the challenges created by AI

Educational Data Mining to Predict Bachelors Students’ Success

Predicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countries’ economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelor’s historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural network’s best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the student’s previous education and engagement with the school environment are decisive in achieving academic success. Doi: 10.28991/ESJ-2023-SIED2-013 Full Text: PD

Factors affecting the deployment of learning analytics in developing countries: case of Egypt

The higher education institutions in developing countries such as Egypt are challenged with the high enrollment student rates, crowded classes and inability to track the progress of each student individually which increased the demand to find a solution that can redeem those problems. Although the usage of learning analytics is an expanding solution to support different educational challenges from performance tracking to detecting students at risks, learning analytics’ developments concentrated on addressing solutions for developed countries. Accordingly, the discipline still requires a broader and indepth interpretation of its contextual usage in developing countries especially Egypt. A research model has been constructed based on literature and tested for its validity and reliability. A questionnaire has been distributed on 148 university students. The study used smart-PLS to interpret and analyze the collected data. The study revealed that organizational culture, data accessibility, trustworthy, visualization has a positive effect on the awareness, while lack of ability has a negative effect on the awareness. Both infrastructure and awareness have significant positive effect on learning analytics impact. The research indicates high learning analytics awareness and high perceived impact on the Egyptian higher education. Evidence should be provided with the collection of more insights from students, faculty members and decision makers

Learning to Prompt in the Classroom to Understand AI Limits: A pilot study

Artificial intelligence's progress holds great promise in assisting society in addressing pressing societal issues. In particular Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems allowing them to process an unprecedented amount of unstructured data. The consequent hype has also backfired, raising negative sentiment even after novel AI methods' surprising contributions. One of the causes, but also an important issue per se, is the rising and misleading feeling of being able to access and process any form of knowledge to solve problems in any domain with no effort or previous expertise in AI or problem domain, disregarding current LLMs limits, such as hallucinations and reasoning limits. Acknowledging AI fallibility is crucial to address the impact of dogmatic overconfidence in possibly erroneous suggestions generated by LLMs. At the same time, it can reduce fear and other negative attitudes toward AI. AI literacy interventions are necessary that allow the public to understand such LLM limits and learn how to use them in a more effective manner, i.e. learning to "prompt". With this aim, a pilot educational intervention was performed in a high school with 30 students. It involved (i) presenting high-level concepts about intelligence, AI, and LLM, (ii) an initial naive practice with ChatGPT in a non-trivial task, and finally (iii) applying currently-accepted prompting strategies. Encouraging preliminary results have been collected such as students reporting a) high appreciation of the activity, b) improved quality of the interaction with the LLM during the educational activity, c) decreased negative sentiments toward AI, d) increased understanding of limitations and specifically We aim to study factors that impact AI acceptance and to refine and repeat this activity in more controlled settings.Comment: Submitted to AIXIA 2023 22nd International Conference of the Italian Association for Artificial Intelligence 6 - 9 Nov, 2023, Rome, Ital