Learning Analytics and Teaching Analytics: The Similarities and Differences

Analytics in education which constitutes of Learning Analytics and Teaching Analytics arouses great attention among researchers and practitioners in the current climate. The use of analytics in education enables educational data to be collected and analysed to serve the needs of all stakeholders to improve the educational process. The present paper gives an overview of Learning Analytics and Teaching Analytics and explores its similarities and differences, as well as the confusion that has been raised between the two defined terms. Alongside, the analytics selection flowchart presented in this paper provides a breakdown on the analytics research direction for Learning Analytics and Teaching Analytics. A deeper and varied understanding of Learning Analytics and Teaching Analytics is imperative for establishing effective and accurate analytical tools alongside with recommendations for improvement in the future

ASPECT-BASED SENTIMENT ANALYSIS FOR UNIVERSITY TEACHING ANALYTICS

Aspect-based sentiment analysis (ABSA) is a natural language processing method to analyze sentiments from large amounts of unstructured text in a much more fine-grained manner at the aspect level. In this research work, we apply it to analyze open text replies from surveys regarding online teaching. Like most other educational institutions, Copenhagen Business School (CBS) had to shift to online teaching from one day to the next. Using ABSA, we investigated the impact of this forced online learning experiment on teaching quality in the spring semester of 2020. Our findings reveal that students disliked online teaching due to insufficient information and unadjusted teaching methods. However, students liked its flexibility and possibility to learn at an individual pace. We show that ABSA can extract valuable information in an easily interpretable manner to support teaching and learning processes. Finally, our findings show that ABSA is a valuable tool to analyze unstructured text quantitatively

Teaching analytics using SAS on Demand for Academics

Faculty teaching courses in business analytics/predictive analytics have a variety of software options to deliver the content of their syllabi. SAS Institute made available to academia a special platform known as SAS on Demand for Academics that allows students and professors to benefit from state of the art analytics software. This teaching note aims at smoothing the learning curve for professors willing to use the SAS on Demand for Academics platform in business analytics classes

A conceptual analytics model for an outcome-driven quality management framework as part of professional healthcare education

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach

Understanding student experience in the age of personalised study

Moves in higher education to provide personalised learning for students increase the importance of gaining and maintaining an understanding of the student experience. For some institutions, this increase in complexity may stretch current systems and data structures. The complexity is amplified where multiple start dates are offered to improve the personalisation of study. The Open University, OU, has over the years, continued to develop its Supported Open Learning, SOL, methods and as an institution is now prioritising Personalised Open Learning, POL. This increases the importance of accessible detailed pathway information. We describe the development of one possible approach intended to provide greater understanding of the student experience for staff interpreting progress data. Another outcome of personalisation is the fragmentation of student cohorts, as individuals each make their own study choices while progressing towards their study goal. A relatively straightforward programme of study can lead to 64 different study routes creating a further challenge for staff in understanding the differing student experiences. We show how this can be represented in a simple data structure that allows powerful queries. Our approach uses a multi-model database, with graphical capabilities. By creating this structure in the ArangoDB environment it was possible to readily test it with 150,000 records and query it using graphical queries in the native AQL language. The early response from faculty colleagues is very positive. They appreciate the graphical output and the ability to straightforwardly answer their questions on whether students experience greater success on one study route rather than another. We are therefore continuing to develop this model to support a qualification review for summer 2018. In our presentation we will describe the challenge and illustrate an approach we are taking: giving examples of the queries we are using and the kinds of data the system outputs

Towards Visual Analytics for Teachers’ Dynamic Diagnostic Pedagogical Decision-Making

The focus of this paper is to delineate and discuss design considerations for supporting teachers\u27 dynamic diagnostic decision-making in classrooms of the 21st century. Based on the Next Generation Teaching Education and Learning for Life (NEXT-TELL) European Commission integrated project, we envision classrooms of the 21st century to (a) incorporate 1:1 computing, (b) provide computational as well as methodological support for teachers to design, deploy and assess learning activities and (c) immerse students in rich, personalized and varied learning activities in information ecologies resulting in high-performance, high-density, high-bandwidth, and data-rich classrooms. In contrast to existing research in educational data mining and learning analytics, our vision is to employ visual analytics techniques and tools to support teachers dynamic diagnostic pedagogical decision-making in real-time and in actual classrooms. The primary benefits of our vision is that learning analytics becomes an integral part of the teaching profession so that teachers can provide timely, meaningful, and actionable formative assessments to on-going learning activities in-situ. Integrating emerging developments in visual analytics and the established methodological approach of design-based research (DBR) in the learning sciences, we introduce a new method called Teaching Analytics and explore a triadic model of teaching analytics (TMTA). TMTA adapts and extends the Pair Analytics method in visual analytics which in turn was inspired by the pair programming model of the extreme programming paradigm. Our preliminary vision of TMTA consists of a collocated collaborative triad of a Teaching Expert (TE), a Visual Analytics Expert (VAE), and a Design-Based Research Expert (DBRE) analyzing, interpreting and acting upon real-time data being generated by students\u27 learning activities by using a range of visual analytics tools. We propose an implementation of TMTA using open learner models (OLM) and conclude with an outline of future work

Development of a contextualised data analytics framework in South African higher education: Evolvement of teacher (teaching) analytics as an indispensable component

Data analytics in higher education aims to address, amongst other matters, student success and the factors related thereto.  Faced by continuous poor student success rates, the Faculty of Economic and Management Sciences at a South African university embarked on the development and implementation of a contextualised data analytics framework to address this problem. Implementation of the framework highlighted the need for the inclusion of teaching (teacher) analytics as an integral part of the framework. Including teaching analytics not only ensured a more comprehensive understanding of the teaching and learning process, but also resulted in unexpected extensions of the framework for the scholarly development of teachers.  Features of the adapted data analytics framework, with a specific focus on the teaching (teacher) analytics component, is presented in this article

Teaching Tip: Hackalytics: Using Computer Hacking to Engage Students in Analytics

The demand for qualified analytics professionals remains high with forecasts showing a continued need over the next few years. While this demand necessitates instruction in analytics in the classroom, many students find analytics concepts to be complicated and boring. This teaching brief describes a novel approach to teaching analytics through computer hacking. Students are exposed to the entire data lifecycle by first collecting intrusion detection data through the hacking of other student machines and then utilizing simple analytics procedures to analyze this data. Qualitative results show that the students enjoy the activity both in terms of the fun of hacking their fellow classmates as well as analyzing this data in an area less utilized in analytics instruction – security analytics. Three levels of the exercise are provided as well as how-to materials for students to run the exercise

How to capitalise on mobility, proximity and motion analytics to support formal and informal education?

© 2017, CEUR-WS. All rights reserved. Learning Analytics and similar data-intensive approaches aimed at understanding and/or supporting learning have mostly focused on the analysis of students' data automatically captured by personal computers or, more recently, mobile devices. Thus, most student behavioural data are limited to the interactions between students and particular learning applications. However, learning can also occur beyond these interface interactions, for instance while students interact face-to-face with other students or their teachers. Alternatively, some learning tasks may require students to interact with non-digital physical tools, to use the physical space, or to learn in different ways that cannot be mediated by traditional user interfaces (e.g. motor and/or audio learning). The key questions here are: why are we neglecting these kinds of learning activities? How can we provide automated support or feedback to students during these activities? Can we find useful patterns of activity in these physical settings as we have been doing with computer-mediated settings? This position paper is aimed at motivating discussion through a series of questions that can justify the importance of designing technological innovations for physical learning settings where mobility, proximity and motion are tracked, just as digital interactions have been so far