Features

AAT makes educational data accessible by allowing users (e.g., learning designers, instructors, etc.) to analyze and investigate these data while guided through step-by-step wizard-like interfaces. During this process, users build queries in stages as they answer a series of questions about the type of data they want to access. Further processing options allow for data analysis and filtering. AAT is built to be fully portable, using a template system to control how it interfaces with the databases of an LMS. This feature ensures that AAT can be customized to access any LMS or version of LMS (even if an LMS has been customized to the needs of an educational institution) using any database management system. User-friendliness and ensuring that the tool is easy to use by people without computer science skills (e.g., programming, SQL, etc.) is key in AAT. For example, AAT uses a unique ontology to translate the database structure into terms that can be understood by users without knowledge of the LMS's database.

Basic Building Blocks

• 1. Project: Creates a container to store all of the selections for a question/query.
• 2. LMS: AAT can access as many LMSs as the university has available.
• 3. DataSet: the list of courses to be investigated to answer a question/query.
• 4. Pattern: includes details on what the question/query is about.
• 5. Limits: filters that narrow the scope of the question/query.
• 6. Chaining: combining two patterns to expand the results.
• 7. Analysis: supports basic functionality to allow users to analyze a pattern in further detail.

Building a Question/Query

• Step 1: Create a Project from the Project Manager page.
• Step 2: Choose an LMS to associate with your project.
• Step 3: Choose an existing DataSet or create a new DataSet that specifies which courses should be included in the investigation.
• Step 4: Create a Pattern that specifies what should be investigated in terms of Concepts (i.e., students, quizzes, forums, etc.) and Attributes (i.e., student id, quiz grades, forum messages, etc.). In addition, limits can be added to use a variety of filters (e.g., include only students with quiz grades lower than 70%, etc.).
• Step 5: Optionally, additional actions can be performed on a pattern, such as Cloning (making a copy of a pattern to edit or share), Chaining (linking two patterns together to expand the results) and Analysis (performing calculations such as average, sum, count, min and max on a result set of a pattern to analyse data in more detail).
• Step 6: View the results of a pattern, or export them in HTML, XML, or CSV format for use in other analysis tools, or for sharing with others.

AAT Demo Video