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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 paper aims to discuss research and design of learning activities involving activity tracking and wearable activity tracking technology.

Three studies are summarized as part of a program of research that sought to design new learning activities for classroom settings. The first used data from a qualitative interview study of adult athletes who self-track. The second used video excerpts from a designed learning activity with a group of fifth grade elementary students. The third study draws largely on quantitative assessment data from an activity tracking unit enactment in a rural sixth grade class.

Activity tracking appears to provide opportunities for establishing benchmarks and calibration opportunities related to intensity of physical activities. Those features of activity tracking can be leveraged to develop learning activities where elementary students discover features of data and how data are affected by different distributions. Students can show significant improvement related to statistical reasoning in classroom instructional units that centralize the use of self-tracked data.

As activity tracking is becoming a more ubiquitous practice with increased pervasiveness and familiarity with mobile and wearable technologies, this paper demonstrates a topical intersection between the information and learning sciences, illustrates how self-tracking can be recruited for instructional settings, and it discusses concerns that have emerged in the past several years as the technology related to activity tracking begins to be used for educational purposes.

This paper presents a part of our research work that places an emphasis on Tracking Data Analysis and Visualization (TrAVis) tools, a web‐based system, designed to enhance online tutoring and learning activities, supported by computer‐mediated communication (CMC) tools. TrAVis is particularly dedicated to assist both tutors and students in the task of exploiting tracking data of communication activities throughout the learning process. This paper focuses on the technical aspects of TrAVis, the visualization of students' tracking data and the experiment we have conducted in an authentic learning situation.

A mixture of iterative and participative approaches has been adopted for the design of TrAVis. Different versions of TrAVis were built during the progress of our research. The major changes in each build have particularly involved the conceptual design of data indicators of students' activities and the visualization techniques of the data indicators. Both case studies and experiments have been made to evaluate TrAVis.

This paper demonstrates how TrAVis provides a new experience in visualizing and analyzing students' tracking data. While it shows the originality and novelty of the system, it also reveals the potential benefits of TrAVis to both tutors and students in their online tutoring and learning activities.

The result from the experiment is not sufficient to evaluate some specific aspects of TrAVis. As a matter of fact, the lack of user's feedback did not enable us to justify whether or not the proposed data indicators would be actually used by the users.

The data indicators shown in this paper are computed based on the real needs of the participants in the learning process. Online questionnaires were used and face‐to‐face interviews have been made to study the needs of the users throughout this research work.

One of the particularities of this research is the proposed system, TrAVis, objectively designed to better support the tutors in the tasks of monitoring and evaluating students on CMC tools. Plus, TrAVis is distinguished from the existing systems by its capacity in computing substantial data indicators, allowing the tutors to efficiently visualize and analyze both the process and the product of students' activities.

The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

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.

The purpose of this paper is to describe a proposal for a data-driven investigation aimed at determining whether students’ learning behavior can be extracted and visualized from action logs recorded by Moodle. The paper also tried to show whether there is a correlation between the activity level of students in online environments and their academic performance with respect to final grade.

The analysis was carried out using log data obtained from various courses dispensed in a university using a Moodle platform. The study also collected demographic profiles of students and compared them with their activity level in order to analyze how these attributes affect students’ level of activity in the online environment.

This work has shown that data mining algorithm like vector space model can be used to aggregate the action logs of students and quantify it into a single numeric value that can be used to generate visualizations of students’ level of activity. The current investigation indicates that there is a lot of variability in terms of the correlation between these two variables.

The value presented in the study can help instructors monitor course progression and enable them to rapidly identify which students are not performing well and adjust their pedagogical strategies accordingly.

A plan to continue the work by developing a complete dashboard style interface that instructors can use is already underway. More data need to be collected and more advanced processing tools are necessary in order to obtain a better perspective on this issue.

Many universities now offer courses online using learning management systems (LMS). Numerous studies have been conducted to assess the effectiveness of the LMS but few studies have examined how student online behavior within the course, or what they think about the online course, are related to their actual learning outcomes. The paper aims to discuss this issue.

In this study, student activity in an online business course was captured though learning analytics and assignments to determine if online activity and reflective learning impact final grade. A post-positivist ideology was employed. The dependent variable was the grade resulting from five assignments assessed using rubrics. Correlation, t-tests, multiple regression, surface response regression, General Linear Model (GLM)/F-tests, text analytics, analysis of means (ANOM), and cluster analysis were used to test the hypotheses.

Four statistically significant predictors of online student learning performance were identified: course logins, lesson reading, lesson quiz activity, and lesson quiz scores. This four factor model captured 78 percent of variance on course grade which is a strong effect and larger than comparative studies using learning analytics with online courses. Text analytics and ANOM conducted on student essays identified 17 reflective learning keywords that were grouped into five clusters to explain online student behavior.

First, from a pedagogy standpoint, encouraging students to complete more online lessons including quizzes, generally promotes learning, resulting in higher grades, which is a win:win for students and for the university. Second, from an IT perspective, the student pre and post testing resulted in statistically significant increase of IT-course knowledge, which puts students on a solid foundation to begin an online business course. Additionally, the link between students voicing IT problems but nonetheless scoring very well on the course certainly implies the development of IT self-efficacy, developed partly through the pre and post testing process. A clear link was established between course learning objectives and student learning performance by using a unique text analytics procedure.

The mixed-methods research design started with hypothesis testing using parametric and nonparametric techniques. Once a statistically significant predictive GLM was developed, qualitative data were collected from what the students thought as expressed in their last essay assignment. Text analytics was used to identify and statistically weight the 17 most frequent reflective learning keywords from student essays. A visual word cloud was presented. Parametric statistics were then used to partition the reflective learning keywords into grade boundaries. Nonparametric cluster analysis was used to group similar reflective keyword-grade associations together to form five clusters. The five clusters helped to explain student online behavior.

This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student performance in different courses? Which machine-learning classifiers tend to perform consistently well across different courses? Can the authors develop a general model for use in multiple courses to predict student performance based on LMS data?

Three mandatory undergraduate courses with large class sizes were selected from three different faculties at a large Western Canadian University, namely, faculties of science, engineering and education. Course-specific models for these three courses were built and compared using data from two semesters, one for model building and the other for generalizability testing.

The investigation has led the authors to conclude that it is not desirable to develop a general model in predicting course failure across variable courses. However, for the science course, the predictive model, which was built on data from one semester, was able to identify about 70% of students who failed the course and 70% of students who passed the course in another semester with only LMS data extracted from the first four weeks.

The results of this study are promising as they show the usability of LMS for early prediction of student course failure, which has the potential to provide students with timely feedback and support in higher education institutions.

As universities advance towards a new data-informed, intra-institutional collaboration paradigm, new roles and services are continuously added to academic library routines. This changing context that exerts considerable stress upon library organizations to prove their value and contributions to student progress is leading the community to start questioning the utility, scope and prospects of patron data collection practices. The study sought library science postgraduate students’ viewpoints about the adequacy and utility of current library use data collection practices in Greek academic libraries. It also aimed to investigate the value, relevance and priority of the integration of library usage data with the rest of university information systems (e.g. learning analytics) along with associated practical and ethical considerations, and advocacy aspects.

Mixed-methods, Web-based survey distributed to postgraduate students during a seminar designed to familiarize them with trends in academic library use data capabilities.

Participants acknowledged that neither policies nor procedures are currently adequate to expand and interconnect their data pools to campus information systems. They were opposed to disclosing personally identifiable patron activity data to faculty, while their opinions were divided as to the use of student activity monitoring technology. Nevertheless, they made several comments on how to mitigate the community's considerations around the implementation of this new data management philosophy in the library and were optimistic about the benefits this development could entail for library visibility and student progress.

Results of this first-time research in the Greek higher education context, revelatory of potential road blockers to upgrading the library use data collection practices, can be of significant value to both curricula developers and university decision-makers who seek ways to prepare the ground for the successful implementation of new operations.

The purpose of this paper is to evaluate four visualizations that represent affective states of students.

An empirical-experimental study approach was used to assess the usability of affective state visualizations in a learning context. The first study was conducted with students who had knowledge of visualization techniques (n=10). The insights from this pilot study were used to improve the interpretability and ease of use of the visualizations. The second study was conducted with the improved visualizations with students who had no or limited knowledge of visualization techniques (n=105).

The results indicate that usability, measured by perceived usefulness and insight, is overall acceptable. However, the findings also suggest that interpretability of some visualizations, in terms of the capability to support emotional awareness, still needs to be improved. The level of students’ awareness of their emotions during learning activities based on the visualization interpretation varied depending on previous knowledge of information visualization techniques. Awareness was found to be high for the most frequently experienced emotions and activities that were the most frustrating, but lower for more complex insights such as interpreting differences with peers. Furthermore, simpler visualizations resulted in better outcomes than more complex techniques.

Detection of affective states of students and visualizations of these states in computer-based learning environments have been proposed to support student awareness and improve learning. However, the evaluation of visualizations of these affective states with students to support awareness in real life settings is an open issue.