A MULTI-LAYERED TAXONOMY OF LEARNING ANALYTICS APPLICATIONS

Digital technologies have become immersed in education systems and the stakeholders have discovered a pervasive need to reform existing learning and teaching practices. Among the emerging educational digital technologies, learning analytics create a disruptive potential as it enables the power of educational decision support, real-time feedback and future prediction. Until today, the field of learning analytics is rapidly evolving, but still immature and especially low on ontological insights. Little guidance is available for educational designers and researchers when it comes to studies applied learning analytics as a method. Hence, this study offers a well-structured multi-layered taxonomy of learning analytics applications for deeper understanding of learning analytics

A Design Methodology for Learning Analytics Information Systems: Informing Learning Analytics Development with Learning Design

The paper motivates, presents and demonstrates a methodology for developing and evaluating learning analytics information systems (LAIS) to support teachers as learning designers. In recent years, there has been increasing emphasis on the benefits of learning analytics to support learning and teaching. Learning analytics can inform and guide teachers in the iterative design process of improving pedagogical practices. This conceptual study proposed a design approach for learning analytics information systems which considered the alignment between learning analytics and learning design activities. The conceptualization incorporated features from both learning analytics, learning design, and design science frameworks. The proposed development approach allows for rapid development and implementation of learning analytics for teachers as designers. The study attempted to close the loop between learning analytics and learning design. In essence, this paper informs both teachers and education technologists about the interrelationship between learning design and learning analytics

A Framework for Designing Learning Analytics Information Systems

Learning analytics offers new opportunities in higher education, yet the design and development of educational data analytics are facing several challenges. Little guidance is available for researchers and developers when it comes to designing, developing, and implementing learning analytics information systems in higher education. Hence, this study proposes a comprehensive conceptual framework for designing learning analytics information systems incorporating both computational and educational aspects. The framework provides systematic support for learning analytics researchers and designers. It is constructed based on the process and critical dimensions of learning analytics and instructional systems design. By applying the framework to analyze a previously published study, we provide a better understanding of its key qualities. Furthermore, the application of the framework to design a new learning analytics information system provides forward engineering support

Technology-Enhanced Learning Environments and Adaptive Learning Systems – Development of Functionality Taxonomies

Especially against the background of the current coronavirus crisis, technology-enhanced learning environments (TELEs) increasingly characterize teaching at universities. For the successful use and integration of TELEs, it is important to understand the functionalities of the technologies used. Based on the state of the art and following [1], we develop two taxonomies. The first taxonomy depicts eleven functionalities with different dimensions relevant for successfully designing TELEs. Sound knowledge of the functionalities supports research on adaptive learning within TELEs and the implementation of student-centered learning opportunities, which is structured in a second functionality taxonomy for adaptive learning systems (ALSs). We contribute to current research on TELEs and ALSs by providing a structured overview of functionalities and suggestions for further research with our research opportunities

Exploring The Effect of Online Course Design on Preservice Teachers’ Knowledge Transfer and Retention Through Learning Analytics

There is a vast amount of data collected on e-learning platforms that can provide insight and guidance to both learners and educators. However, this data is rarely used for evaluation and understanding the learning process. Hence, to fill this gap in the literature this study explored the effect of online course design on students’ transfer and retention of knowledge through learning analytics. The aim was to reveal study behaviours of participants over a short time while exploring their academic performance. Using a mixed method approach, this research is conducted in two different countries in a limited time. The results showed that the more times students visited the learning module and the longer these visits, the higher the students’ transfer knowledge scores in this module. Most importantly, the only variable found to be a significant predictor of students’ transfer learning outcome was the number of sessions in the module website

Data Analytics in Higher Education: An Integrated View

Data analytics in higher education provides unique opportunities to examine, understand, and model pedagogical processes. Consequently, the methodologies and processes underpinning data analytics in higher education have led to distinguishing, highly correlative terms such as Learning Analytics (LA), Academic Analytics (AA), and Educational Data Mining (EDM), where the outcome of one may become the input of another. The purpose of this paper is to offer IS educators and researchers an overview of the current status of the research and theoretical perspectives on educational data analytics. The paper proposes a set of unified definitions and an integrated framework for data analytics in higher education. By considering the framework, researchers may discover new contexts as well as areas of inquiry. As a Gestalt-like exercise, the framework (whole) and the articulation of data analytics (parts) may be useful for educational stakeholders in decision-making at the level of individual students, classes of students, the curriculum, schools, and educational systems

Adoption of big data analytics and its impact on organizational performance in higher education mediated by knowledge management

Due to SARS-CoV-2 pandemic, higher education institutions are challenged to continue providing quality teaching, consulting, and research production through virtual education environments. In this context, a large volume of data is being generated, and technologies such as big data analytics are needed to create opportunities for open innovation by obtaining valuable knowledge. The purpose of this paper was to investigate the factors that influence the adoption of big data analytics, as well as to evaluate the relationship it has with performance and knowledge management, taking into consideration that this technology is in its initial stages and that previous research has provided varied results depending on the sector in focus. To address these challenges, a theoretical framework was developed to empirically test the relationship of these variables. A total of 265 members of universities in Latin America were surveyed and structural equation modeling was used for hypothesis testing. The findings identified compatibility, an adequate organizational data environment, and external support as factors required to adopt big data analytics and their positive relationship is tested with knowledge management processes and organizational performance. This study provides practical guidance for decision-makers involved in or in charge of defining the implementation strategy of big data analytics in higher education institutions.Debido a la pandemia del SARS-CoV-2, las instituciones de educación superior tienen el desafío de continuar brindando enseñanza, consultoría y producción de investigación de calidad a través de entornos educativos virtuales. En este contexto, se está generando un gran volumen de datos y se necesitan tecnologías como la analítica de Big Data para crear oportunidades de innovación abierta mediante la obtención de conocimientos valiosos. El propósito de este trabajo fue investigar los factores que influyen en la adopción de la analítica de Big Data, así como evaluar la relación que tiene con el desempeño y la gestión del conocimiento, tomando en consideración que esta tecnología se encuentra en sus etapas iniciales y que investigaciones previas han proporcionado resultados variados según el sector en cuestión. Para abordar estos desafíos, se desarrolló un marco teórico que comprobó empíricamente la relación de estas variables; se encuestaron a 265 miembros de universidades de América Latina y se utilizó el modelado de ecuaciones estructurales. Los hallazgos identificaron a la compatibilidad, un entorno de datos organizacional adecuado y el apoyo externo como factores necesarios para adoptar la analítica de Big Data y se comprobó su relación positiva con los procesos de gestión del conocimiento y el desempeño organizacional. Este estudio proporciona una guía práctica para los tomadores de decisión involucrados o encargados de definir la estrategia de implementación de la analítica de Big Data en instituciones de educación superior

Perspectives of IR Professionals Regarding the Impact of Data Analytic Systems on Institutional Decision- Making.

The capacity for data analytical decision-making is not always optimal in institutions of higher education (Hawkins & Bailey, 2020). Data analytic decision making for this study is defined as any decision utilized to improve the process or outcome for any function of higher educational administration (Nguyen et al., 2020) including but not limited to: state appropriated funding (e.g. Campbell, 2018) improving graduation rates (e.g Moscoso-Zea, Saa & Luján-Mora, 2019), teacher instruction (e.g. Cai & Zhu, 2015), or student success (e.g. Foster & Francis, 2020). Many IR professionals still face obstacles pertaining to their ability to both utilize data analytical software as well as share data analytical findings across their respective clientele units outside of institutional research to impact institutional decision-making (Lehman, 2017). The literature is lacking concerning how IR professionals experience and navigate these critical aspects of data analytical decision-making support in higher educational institutions. The purpose of this study was to address the gap in the research by assessing the perspectives of IR professionals regarding their ability to utilize data analytic systems (e.g., analyzing, interpreting, sharing of data) to impact and strengthen institutional decision-making. The purpose of this study was also to understand how institutional culture (e.g., policies, operational processes, relevancy, conduciveness) influences the ability of IR professionals to utilize data analytic systems when sharing data findings or collaborating across their respective institutions to enhance institutional decision-making. Recommendations based on the study findings included stronger data governance for dashboards and data visualizations, expanding predictive analytics to enhance student success, and data literacy training with both utilizing data analytics software and interpreting data findings according to the context of individual institutions

Teaching analytics and teacher dashboards to visualise SET data: Implication to theory and practice

Teaching Analytics (TA) is an emergent theoretical approach that combines teaching expertise, visual analytics, and design-based research to support teachers' diagnostic pedagogical ability to use data as evidence to improve teaching quality. The thesis is focused on designing dashboards to help teachers visualise Student Evaluation of Teaching (SET) data as a form of TA for improving the quality of teaching. The research examined the role of TA by deploying customisable dashboards to support teachers in using data to design and facilitate learning. The researcher carried out an integrated literature review to explore the notion of TA and SET data. Moreover, a Data Science Life Cycle model was proposed to guide teachers and researchers using SET data to improve learning and teaching quality. The research comprised several phases. In phase I, a simulated data technique was used to generate SET scores that informed the development of a preliminary teacher dashboard. Phase II surveyed teachers' use of SET data. The survey results indicated that more than half of the participants used SET for improving teaching practice. The research also showed that participants valued the free-text qualitative comments in SET data. Hence, phase III collected real free-text qualitative comments in SET data on students' perceptions of a previously tutored course. The survey results further indicated that although teachers were unaware of a dashboard's value in presenting data, they wanted to visualise SET data using dashboards. Phase IV redesigned the preliminary dashboards to present the real SET data and the simulated SET scores. Finally, phase V carried out usability testing to evaluate teachers' perceptions of usability and usefulness of the teacher's dashboards. Overall, the result of the usability study indicated the perceived value of the teacher's dashboards