Academic Analytics and Data Mining in Higher Education

The emerging fields of academic analytics and educational data mining are rapidly producing new possibilities for gathering, analyzing, and presenting student data. Faculty might soon be able to use these new data sources as guides for course redesign and as evidence for implementing new assessments and lines of communication between instructors and students. This essay links the concepts of academic analytics, data mining in higher education, and course management system audits and suggests how these techniques and the data they produce might be useful to those who practice the scholarship of teaching and learning

Stability and sensitivity of Learning Analytics based prediction models

Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation

Current Issues in Emerging eLearning, Volume 7, Issue 1: APLU Special Issue on Implementing Adaptive Learning At Scale

The second of two Specials Issues of the CIEE journal to have been produced and guest edited by the Personalized Learning Consortium (PLC) of the Association of Public and Land-grant Universities (APLU), featuring important research resulting from university initiatives to launch, implement and scale up the use of adaptive courseware and the strategies of adaptive learning

A Transformative Approach to Incorporating Adaptive Courseware: Strategic Implementation, Backward Design and Research-based Teaching Practices

In July 2016, Colorado State University (CSU) joined seven other land-grant institutions in the Accelerating Adoption of Adaptive Courseware grant sponsored by the Personalized Learning Consortium (PLC) of the Association of Public and Land-grant Universities (APLU). A primary objective of the grant was to scale the adoption of adaptive courseware in general education courses at each of the grant institutions. CSU targeted high-enrollment, general education courses and took a three-pronged, transformative approach to the integration of adaptive courseware. Specifically, CSU divided the courseware integration into three components: 1) strategic implementation of courseware, 2) backward course design, and 3) incorporation of research-based teaching practices. By May 2020, it is projected that over 40,000 students will have taken courses that were developed in this manner. Faculty participating in the grant completed the Teaching Practices Inventory (TPI) developed by the Wieman Institute. The inventory measures the extent to which instructors use research-based teaching practices (ETP). Faculty use of research-based teaching practices in strategic alignment with active learning and adaptive courseware provided the greatest measure of success. In general, instructors with ETP scores above 24 had higher course success rates than those with lower ETP scores. However, these differences were statistically significant for instructors of STEM courses with ETP scores of 30 and higher. Data indicates that simply adding adaptive courseware is not enough to impact student success. It is the combination of: 1) strategic implementation of courseware, 2) backward course design, and 3) the incorporation of research-based teaching practices that has the most potential to impact student success

A review of ten years of implementation and research in aligning learning design with learning analytics at the Open University UK

There is an increased recognition that learning design drives both student learning experience and quality enhancements of teaching and learning. The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, the OU has been unpacking the complexity of instructional practices, as well as providing conceptual and empirical evidence of how learning design influences student behaviour, satisfaction, and performance. This study discusses the implementation of learning design at the OU in the last ten years, and critically reviews empirical evidence from eight recent large-scale studies that have linked learning design with learning analytics. Four future research themes are identified to support future adoptions of learning design approaches

Innovation through the flipped model of learning: enriching students' and instructors' experience

The paper provides a brief literature review of the FML, a description of the FML experience within the context of a multi-year project in a major research university – from designing to producing and integrating it into the second-language writing curriculum – and recommendations for scalable implementation. Special attention is given to the benefits of this approach for students as well as to its broader pedagogical advantages.Published versio

Conducting a Formative Evaluation on a Course-Level Learning Analytics Implementation Through the Lens of Self-Regulated Learning and Higher-Order Thinking

Self-regulated learning (SRL) and higher-order thinking skills (HOTS) are associated with academic achievement, but fostering these skills is not easy. Scholars have suggested an alternative way to scaffold these important skills through learning analytics (LA). This paper presents a formative evaluation of a course-level LA implementation through the lens of self-regulated learning (SRL) and higher-order thinking skills (HOTS). We explored the changes in students’ SRL, HOTS, and perceptions at the end of the course term. Results indicate an increase in some elements of SRL and HOTS, and positive student perceptions. Discussion on implications and opportunities for informing future teaching strategies and course design reiteration are included

Beyond Description: Converting Web Site Usage Statistics into Concrete Site Improvement Ideas

Web site usage statistics are a widely used tool for Web site development, but libraries are still learning how to use them successfully. This case study summarizes how Morris Library at Southern Illinois University Carbondale implemented Google Analytics on its Web site and used the reports to inform a site redesign. As the main campus library at a research university with about 20,000 undergraduate and graduate students, the library included resources from multiple library departments on a single site. In planning the redesign, Morris Library\u27s Virtual Library Group combined usage reports with information from other sources, such as usability tests and user comments. The Virtual Library Group faced barriers to interpreting and applying the usage statistics in the site redesign, including some that were specific to the library\u27s implementation of the Google Analytics tool and some limitations inherent with Web usage statistics in general. Some key barriers in applying the usage statistics to a redesign included sifting through data that did not have implications for the site redesign, interpreting the implications of usage numbers for the site redesign, and balancing competing interests within the library. Nevertheless, the usage statistics enabled the Virtual Library Group to make better decisions by providing a source of factual information about the site\u27s use rather than relying on staff members’ opinions and conjectures

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning