Cindy Ives

A Flexible Mechanism for Providing Adaptivity Based on Learning Styles in Learning Management Systems

While today’s learning management systems (LMSs) provide lot of support for teachers to assist them in holding online courses, they typically do not consider students’ individual differences in the composition and structure of courses. In this paper, we introduce a mechanism for extending LMSs’ functionality to provide learners with courses that fit their individual learning styles, using adaptive sorting and adaptive annotation in order to highlight the learning objects (LOs) that support students’ learning process the best. The mechanism enables teachers to add adaptivity to their already existing courses, using a flexible course structure in order to avoid limiting the richness of the learning resources and materials. Besides being flexible to teachers’ needs, the adaptive mechanism aims at asking teachers for as little as possible additional effort when using it, requiring teachers only to choose the corresponding type of LO when creating an LO in the authoring tool of the LMS.

Planning for Integrating Teaching Technologies

Teaching technologies offer pedagogical advantages which vary with specific contexts. Successfully integrating them hinges on clearly identifying pedagogical goals, then planning for the many decisions that technological change demands. In examining different ways of organizing this process, we have applied planning tools from other domains - Fault Tree Analysis and Capability Maturity Modeling- at the school and college levels. In another approach, we have examined attempts to broadly model the integration process at the university level. Our studies demonstrate that the use of a variety of tools and techniques can render the integration of teaching technologies more systematic.

Building a data governance model for learning analytics

This international panel presentation aims to explore and discuss the issues that emerge when an educational institution decides to develop learning analytics initiatives. While learning analytics may provide data that lead to improvements in the quality of teaching and learning design, and therefore has the potential to enhance the overall quality of education, the successful development and implementation of tools and processes for learning analytics are complex and problematic. In this panel, data governance considerations will be discussed from organizational, ethical, learning design, and technical points of view.

Extending the AAT Tool with a User-Friendly and Powerful Mechanism to Retrieve Complex Information from Educational Log Data

In online learning, educators and course designers traditionally have difficulty understanding how educational material is being utilized by learners in a learning management system (LMS). However, LMSs collect a great deal of data about how learners interact with the system and with learning materials/activities. Extracting this data manually requires skills that are outside the domain of educators and course designers, hence there is a need for specialized tools which provide easy access to these data. The Academic Analytics Tool (AAT) is designed to allow users to investigate elements of effective course designs and teaching strategies across courses by extracting and analysing data stored in the database of an LMS. In this paper, we present an extension to AAT, namely a user-friendly and powerful mechanism to retrieve complex information without requiring users to have background in computer science. This mechanism allows educators and learning designers to get answers to complex questions in an easy understandable format.

The Academic Analytics Tool: Workflow and Use Cases

To meet the demand for timely analysis and revision of online courses, educators need ongoing, unfettered access to data about how students interact with courses and online resources. Currently available tools for exploring student data provide some important insights, but are typically focused on automated data mining, visualizations, or displaying pre-set reports. These tools also often require either high technical skills and/or installation of specialized software, making them inaccessible to most educators and learning designers. In this paper, we introduce the Academic Analytics Tool (AAT) and provide some hands-on examples on how the tool can be used. AAT is designed to allow people (e.g., educators, learning designers, etc.) without technical expertise to extract and analyse data from learning management systems (LMSs). AAT offers high usability and permits full exploration of LMSs’ data on any computer with internet access to foster responsive analysis and improvement of online courses.