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The purpose of this paper is to present the METAL project, a French open learning analytics (LA) project for secondary school, that aims at improving the quality of teaching. The originality of METAL is that it relies on research through exploratory activities and focuses on all the aspects of a learning analytics environment.

This work introduces the different concerns of the project: collection and storage of multi-source data owned by a variety of stakeholders, selection and promotion of standards, design of an open-source LRS, conception of dashboards with their final users, trust, usability, design of explainable multi-source data-mining algorithms.

All the dimensions of METAL are presented, as well as the way they are approached: data sources, data storage, through the implementation of an LRS, design of dashboards for secondary school, based on co-design sessions data mining algorithms and experiments, in line with privacy and ethics concerns.

The issue of a global dissemination of LA at an institution level or at a broader level such as a territory or a study level is still a hot topic in the literature, and is one of the focus and originality of this paper, associated with the large spectrum of different concerns.

Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases.

The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.

Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction.

Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

In spite of the popularity of learning analytics (LA) in higher education institutions (HEIs), the success rate and value gained through LA projects is still little and unclear. The existing research on LA focusses more on tactical capabilities rather than its effect on organizational value. The key questions are what are the expected benefits for the institution? And how the investment in LA can bring tangible value? In this research, the authors proposed a value realization framework from LA extending the existing framework of information technology value.

The study includes a detailed literature review focusing on the importance, existing frameworks and LA adoption challenges. Based on the identified research gap, a new framework is designed. The framework depicts the several constructs and their relationships focusing on strategic value realization. Furthermore, this study includes three case studies to validate the framework.

The framework suggests that leveraging LA for strategic value demands adequate investment not only in data infrastructure and analytics but also in staff skill training and development and strategic planning. Universities are required to measure the strategic role of LA and spend wisely in quality data, analytical tools, skilled staff who are aware of the latest technologies and data-driven opportunities for continuous improvement in learning.

The framework permits education leaders to design better strategies for attaining excellence in learning and teaching, and furnish learners with new data to settle on the most ideal decisions about learning. The authors believe that the appropriation of this framework and consistent efficient interest in learning analytics by the higher education area will prompt better results for learners, colleges and more extensive society. The research also proposes two approaches and eleven research agendas for future research based on the framework. The first is based on the constructs and their relationships in LA value creation, whereas the later one focusing on identifying problems associate with it.

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.

This study aims to focus on providing a computerized classification testing (CCT) system that can easily be embedded as a self-assessment feature into the existing legacy environment of a higher education institution, empowering students with self-assessments to monitor their learning progress and following strict data protection regulations. The purpose of this study is to investigate the use of two different versions (without dashboard vs with dashboard) of the CCT system during the course of a semester; to examine changes in the intended use and perceived usefulness of two different versions (without dashboard vs with dashboard) of the CCT system; and to compare the self-reported confidence levels of two different versions (without dashboard vs with dashboard) of the CCT system.

A total of N = 194 students from a higher education institution in the area of economic and business education participated in the study. The participants were provided access to the CCT system as an opportunity to self-assess their domain knowledge in five areas throughout the semester. An algorithm was implemented to classify learners into master and nonmaster. A total of nine metrics were implemented for classifying the performance of learners. Instruments for collecting co-variates included the study interest questionnaire (Cronbach’s a = 0. 90), the achievement motivation inventory (Cronbach’s a = 0. 94), measures focusing on perceived usefulness and demographic data.

The findings indicate that the students used the CCT system intensively throughout the semester. Students in a cohort with a dashboard available interacted more with the CCT system than students in a cohort without a dashboard. Further, findings showed that students with a dashboard available reported significantly higher confidence levels in the CCT system than participants without a dashboard.

The design of digitally supported learning environments requires valid formative (self-)assessment data to better support the current needs of the learner. While the findings of the current study are limited concerning one study cohort and a limited number of self-assessment areas, the CCT system is being further developed for seamless integration of self-assessment and related feedback to further reveal unforeseen opportunities for future student cohorts.

The purpose of this paper is to propose a conceptual model of engagement, appropriated from social media marketing, as a sense-making framework to understand engagement as a measurable process through the development of engagement profiles. To explore its potential application to education the paper follows previous work with Personalised Learning strategies to place emphasis on the promotion of the learner voice – their ability to influence decisions affecting them and their community.

This paper will position engagement as a sociocultural process and adopt an Activity Theory based methodology demonstrated through a desk analysis of VLE data from a further education college.

The analysis suggests that the approach can yield insights that may be elusive in traditional measures reinforcing the overall conceptual proposal for a multi-method approach to profiling learner engagement.

The paper has focused on presentation and exploration of the conceptual approach, which has limited the scope to broaden the discussion of the desk analysis and wider findings that this approach reveals.

It is intended that the approach offers a generalizable model that can be adopted by institutions planning to measure engagement or develop learner activity profiles. Several areas of immediate potential are identified throughout the paper.

This paper contributes a multi-method approach to engagement as argued for in recent engagement literature. This should offer institutions a way to realise value from emerging ideas within related domains of Learning Design and Learning Analytics.

Many teachers and students in the USA and various parts of the world are migrating some aspects of education online out of necessity. The purpose of this paper is to identify and describe strategies of the self-regulated learning (SRL) framework for K-12 students learning in online environments to support remote learning with online and digital tools during the COVID-19 pandemic.

The SRL framework (Zimmerman, 2008) has been used consistently to support students in learning to work independently. This framework highlights three phases: planning, performing and evaluating. Previous research in K-12 online learning has yielded specific strategies that are useful. The paper identified and described the strategies to an audience seeking answers on how to meet the needs of students in online learning environment.

The main types of strategies that have emerged from previous studies include asking students to consider how they learn online, providing pacing support, monitoring engagement and supporting families.

Although the social crisis of COVID-19 is unique, prior research in online learning may be useful for supporting teacher practice and suggesting future research. Developing SRL skills of students will ensure the effectiveness of online learning that the field of education may ultimately focus on in the future.