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This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.

The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets.

Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them.

To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.

This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods.

Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables.

The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria.

This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

The study aims to propose a predictive model that combines personality and demographic factors to predict student academic performance (SAP). This research study works on understanding, enhancing and applying techniques to enhance the prediction of SAP.

The authors gathered information from 305 university students from Al-Zintan University Libya. The study uses a survey questionnaire to collect data on essential variables. The purpose of the questionnaire is to discover variables that affect students' academic performance. The survey questionnaire has 44 closed questions with Likert scale designs that were distributed to a variety of college students at the start of the first semester of 2022. It includes questions about demographics, personality, employment and institutional aspects. The authors proposed a predictive model to identify the main fundamental components, consisting of one dependent variable (SAP) and five independent constructs. The suggested model is tested using partial least squares (PLS) and structural equation modeling (SEM), which perform better than covariance-based structural equation modeling (CB-SEM). PLS-SEM performs well with smaller sample sizes, even for complicated models.

The study results show that the proposed model accurately predicted the student's academic performance. The personality trait variables are a key factor that determines the actual student's academic performance. The student's academic performance is significantly impacted by each variable in the personality trait variables as well.

The process of validating research was done empirically through the accuracy and efficiency of model performance. The study differs from previous studies in that it accumulated a wide range of factors from different dimensions, including student demographics and personality trait factors. The authors developed a structural equation model to predict students' academic performance.

It has been demonstrated that AI-powered, data-driven tools’ usage is not universal, but deeply linked to socio-cultural contexts. The purpose of this paper is to display the need of adopting situated lenses, relating to specific personal and professional learning about data protection and privacy.

The authors introduce the results of a case study based on a large educational intervention at a fully online university. The views of the participants from degrees representing different knowledge areas and contexts of technology adoption (work, education and leisure) were explored after engaging in the analysis of the terms and conditions of use about privacy and data usage. After consultation, 27 course instructors (CIs) integrated the activity and worked with 823 students (702 of whom were complete and correct for analytical purposes).

The results of this study indicated that the intervention increased privacy-conscious online behaviour among most participants. Results were more contradictory when looking at the tools’ daily usage, with overall positive considerations around the tools being mostly needed or “indispensable”.

Though appliable only to the authors’ case study and not generalisable, the authors’ results show both the complexity of privacy views and the presence of forms of renunciation in the trade-off between data protection and the need of using a specific software into a personal and professional context.

This study provides an example of teaching and learning activities that supports the development of data literacy, with a focus on data privacy. Therefore, beyond the research findings, any educator can build over the authors’ proposal to produce materials and interventions aimed at developing awareness on data privacy issues.

Developing awareness, understanding and skills relating to data privacy is crucial to live in a society where digital technologies are used in any area of our personal and professional life. Well-informed citizens will be able to obscure, resist or claim for their rights whenever a violation of their privacy takes place. Also, they will be able to support (through adoption) better quality apps and platforms, instead of passively accepting what is evident or easy to use.

The authors specifically spot how students and educators, as part of a specific learning and cultural ecosystem, need tailored opportunities to keep on reflecting on their degrees of freedom and their possibilities to act regarding evolving data systems and their alternatives.

Concept maps are popularly used within academic development spaces, especially to teach new concepts at beginner levels for undergraduate students. Their popularity is partly based on the fact that they employ visual tools such as charts, diagrams, pictures, tables, etc., to simplify concepts that students would otherwise consider dense. This paper reports on the findings of an extended orientation project conducted between February and June of 2022 with a small cohort of 15 first-year students registered in an entrepreneurship course at a vocational higher education institution in South Africa. The research question guiding this study is: How can concept maps inspire entrepreneurial thinking for first-year ECP students at a vocational institution in South Africa? Using Cultural-Historical Activity Theory (CHAT), I analysed the two iterations of the students' concept maps together with selected data from the focus group interviews. Key findings reported include the students' fuzzy knowledge of what entrepreneurship as a discipline entails, the planned career trajectories for most of the participating students, as well as indecisiveness as to whether the students will be pursuing entrepreneurship after graduation. In the language of CHAT, the above findings are described as presenting tensions between the subject, tool and object. This layer of analysis calls for an urgent re-think of how the students are recruited and orientated into the programme and how the curriculum is delivered at the first-year level.

This paper aims to explore how critical digital and data literacies are facilitated by testing different methods in the classroom, with the ambition to find a pedagogical framework for prompting sustained critical literacies.

This contribution draws on a 10-year set of critical pedagogy experiments conducted in Denmark, USA and Italy, and engaging more than 1,500 young adults. Multi-method pedagogical design trains students to conduct self-oriented guided autoethnography, situational analysis, allegorical mapping, and critical infrastructure analysis.

The techniques of guided autoethnography for facilitating sustained data literacy rely on inviting multiple iterations of self-analysis through sequential prompts, whereby students move through stages of observation, critical thinking, critical theory-informed critique around the lived experience of hegemonic data and artificial intelligence (AI) infrastructures.

Critical digital/data literacy researchers should continue to test models for building sustained critique that not only facilitate changes in behavior over time but also facilitate citizen social science, whereby participants use these autoethnographic techniques with friends and families to build locally relevant critique of the hegemonic power of data/AI infrastructures.

The proposed literacy model adopts a critical theory stance and shows the value of using multiple modes of intervention at micro and macro levels to prompt self-analysis and meta-level reflexivity for learners. This framework places critical theory at the center of the pedagogy to spark more radical stances, which is contended to be an essential step in moving students from attitudinal change to behavioral change.

This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning.

The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use.

The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes.

This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.

Physical activity is an eminent practice for the maintenance of physical and mental health. Physical fitness always enables students to perform their various tasks efficiently specifically in academic performance. Student health is a principal factor for the performance of all kinds of activities particularly in academic performance. This study aimed to evaluate physical activity among university students and the factors for being inactive.

A questionnaire was designed and distributed among the students via WhatsApp, Skype and email, however, some responses were collected physically to maintain the quality of data. The questionnaire was categorized into demographic factors, current physical activity status and reasons for physical inactivity. Each section was further divided into questions and total 24 questions were asked from each individual for evaluation of inactiveness.

The finding of this research explored that enormous number of students are doing part time jobs and unable to find enough time for relaxation. The research was limited and evaluated limited factors and explored that 60.6% of students had less time for entertainment and this is only 2 h for this 60.6% of students. Among all factors of physical activity walking was preferable for 48.34% of the students. Students want to participate in healthy activities. Moreover, students are unable to perform physical exercise due to busy schedules of jobs (47.02%), part-time job burden (15.89%), study burden (35.10%) and poor health conditions (1.32%).

This study concluded that higher percentage of students have stress of limited resources and under this stress condition they are unable to take a balanced diet which they considered cost-effective. They did not perform maximum in their academic and daily activities and did not participate in sports activities. It was also observed that institutions did not provide a sufficient platform for physical activity for students. The research shows the factors which affect the student’s academic performance as being physically inactive. Evaluation of results explored that numerous students have limited resources during their career development and their attention remain diverted to overcome their limited resources which keep them physically unfit. The findings also explored that physically inactive students have heavy study and job burden which is not overcome and analyzed by their institutions.

This study aims to test the mediated effect of school climate in the relationship of principal time use (PTU) to student academic achievement using data from Singapore's 2011 Trends in International Mathematics and Science Study (TIMSS).

Capitalizing on the large-scale data, the study examined how Singapore principals distributed their time across 13 leadership activities and tested using structural equation modeling (SEM) how Singapore principals' time use was related to student achievement through molding intermediate school conditions, such as school climate.

The authors' findings suggest that Singaporean principals, when allocating time, generally emphasized (1) monitoring students' learning progress, (2) promoting school vision, (3) developing curricular goals and (4) monitoring teachers in areas related to curricular goals. Furthermore, after classifying the 13 principal activities into three broad domains, the authors find that Singaporean principals prioritize the domain of vision and goals over the other two domains – facilitating teaching and learning through a safe, positive school environment and problem-solving with teachers, as well as self and organizational improvement. Finally, the authors' SEM manifested a partial mediated model, suggesting principals' strategic time use could serve as a malleable factor in yielding optimal student outcomes through developing a positive school climate.

This study at present is one of the early attempts linking PTU, intermediate school conditions (e.g. school climate) and student outcomes using a mediated design and corresponding statistical modeling.