Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

Our world is significantly shaped by digitalization, fostering new opportunities for technology-mediated learning. Therefore, massive amounts of knowledge become available online. However, concurrently these formats entail less interaction and guidance from lecturers. Thus, learners need to be supported by intelligent learning tools that provide suitable knowledge in a tailored way. In this context, the use of learning analytics in its multifaceted forms is essential. Existing literature shows a proliferation of learning analytics use cases without a systematic structure. Based on a structured literature review of 42 papers we organized existing literature contributions systematically and derived four use cases: learning dashboards, individualized content, tutoring systems, and adaptable learning process based on personality. Our use cases will serve as a basis for a targeted scientific discourse and are valuable orientation for the development of future learning analytics use cases to give rise to the new form of Learning Experience Platforms

A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education

Conceptual framework for process-oriented feedback through Learning Analytics Dashboards

The number of students enrolled in online higher education courses is increasing, and as a result, more data on their learning process is being generated. By exploring this student behavior data through learning analytics, both student and teacher can be provided with process-oriented feedback in the form of dashboards. However, little is known about the typology of relevant feedback in the dashboard to different learning objectives, students and teachers. Although most dashboards and the feedback they provide are based solely on student performance indicators, research shows that such feedback is not sufficient. This article attempts to define a conceptual model that visualizes the relationships between the design of a Learning Analytics Dashboard (LAD) and the concepts of learning science in order to provide process-oriented feedback that supports the regulation of learning. The aim of the work is not to propose a specific design of the LAD to provide feedback, but rather a conceptual framework for the choice of concepts for that design, and therefore to help understand future data needs as a basis for the educational feedback of the dashboards. As a conclusion of our research, we can say that having LADs adapted to any profile (student, teacher, etc.) can improve decision-making processes by showing each user the information that interests them most in the way that best enables them to understand it

Effective usage of Learning Analytics: What do practitioners want and where should distance learning institutions be going?

The implementation of learning analytics may empower distance learning institutions to provide real-time feedback to students and teachers. Given the leading role of the Open University UK (OU) in research and application of learning analytics, this study aims to share the lessons learned from the experiences of 42 participants from a range of faculty, academic and professional positions, and expertise with learning analytics. Furthermore, we explored where distance learning institutions should be going next in terms of learning analytics adoption. The findings from the Learning Analytics User Stories (LAUS) workshop indicated that four key areas where more work is needed: communication, personalisation, integrated design, and development of an evidence-base. The workshop outputs signalled the aspiration for an integrated analytics system transcending the entire student experience, from initial student inquiry right through to qualification completion and into life-long learning. We hope that our study will spark discussion on whether (or not) distance learning institutions should pursue the dream of an integrated, personalised, and evidence-based learning analytics system that clearly communicates useful feedback to staff and students, or whether this will become an Orwellian nightmare

Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach

Se considera que Learning Analytics (LA) es una estrategia prometedora para abordar los desafíos educativos persistentes en América Latina, como las disparidades de calidad y las altas tasas de deserción. Sin embargo, las universidades latinoamericanas se han retrasado en la adopción de LA en comparación con las instituciones de otras regiones. Para comprender las necesidades de los interesados ​​de los servicios de AL, este estudio utilizó métodos mixtos para recopilar datos en cuatro universidades latinoamericanas. Se obtuvieron datos cualitativos de 37 entrevistas con gerentes y 16 grupos focales con 51 docentes y 45 estudiantes, mientras que los datos cuantitativos se obtuvieron de encuestas respondidas por 1884 estudiantes y 368 docentes. De acuerdo con la triangulación de ambos tipos de evidencia, encontramos que (1) los estudiantes necesitan comentarios de calidad y apoyo oportuno, (2) el personal docente necesita alertas oportunas y evaluaciones de desempeño significativas, y (3) los gerentes necesitan información de calidad para implementar intervenciones de apoyo. Por lo tanto, LA ofrece la oportunidad de integrar la toma de decisiones basada en datos en las tareas existentes. © 2020 Elsevier Inc.Learning Analytics (LA) is perceived to be a promising strategy to tackle persisting educational challenges in Latin America, such as quality disparities and high dropout rates. However, Latin American universities have fallen behind in LA adoption compared to institutions in other regions. To understand stakeholders' needs for LA services, this study used mixed methods to collect data in four Latin American Universities. Qualitative data was obtained from 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students, whereas quantitative data was obtained from surveys answered by 1884 students and 368 teaching staff. According to the triangulation of both types of evidence, we found that (1) students need quality feedback and timely support, (2) teaching staff need timely alerts and meaningful performance evaluations, and (3) managers need quality information to implement support interventions. Thus, LA offers an opportunity to integrate data-driven decision-making in existing tasks. © 2020 Elsevier Inc

Curricular Concept Maps as Structured Learning Diaries : Collecting Data on Self-Regulated Learning and Conceptual Thinking for Learning Analytics Applications

The collection and selection of the data used in learning analytics applications deserve more attention. Optimally, selection of data should be guided by pedagogical purposes instead of data availability. Using design science research methodology, we designed an artifact to collect time-series data on students’ self-regulated learning and conceptual thinking. Our artifact combines curriculum data, concept mapping, and structured learning diaries. We evaluated the artifact in a case study, verifying that it provides relevant data, requires a limited amount of effort from students, and works in different educational contexts. Combined with learning analytics applications and interventions, our artifact provides possibilities to add value for students, teachers, and academic leaders.The collection and selection of the data used in learning analytics applications deserve more attention. Optimally, selection of data should be guided by pedagogical purposes instead of data availability. Using design science research methodology, we designed an artifact to collect time-series data on students’ self-regulated learning and conceptual thinking. Our artifact combines curriculum data, concept mapping, and structured learning diaries. We evaluated the artifact in a case study, verifying that it provides relevant data, requires a limited amount of effort from students, and works in different educational contexts. Combined with learning analytics applications and interventions, our artifact provides possibilities to add value for students, teachers, and academic leaders.Peer reviewe

Smart Learning

Artificial intelligence applied to the educational field has a vast potential, especially after the e ects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutorial e ort is higher than in the traditional face-to-face approach. Thus, educational systems are claiming smarter learning technologies that do not pretend to substitute the faculty but make their teaching activities easy. This Special Issue is oriented to present a collection of papers of original advances in educational applications and services propelled by artificial intelligence, big data, machine learning, and deep learning