Metafora: A Web-based Platform for Learning to Learn Together in Science and Mathematics

This paper presents Metafora, both a platform for integrated tools as well as an emerging pedagogy for supporting Learning to Learn Together in science and mathematics education. Our goal is to design technology that brings education to a higher level; a level where students not only learn a subject matter, but also gain a set of critical skills needed to engage in and self-regulate collaborative learning experiences in science and math education. We first discuss the core skills we hope students will gain as they learn to learn together. We then present our design and implementation that can achieve this goal; a platform and pedagogy we have developed to support the learning of these skills. Finally, we present an example use of our system based on results from pilot studies that demonstrates interaction with the platform, and potential benefits and limitations of the tools in promoting the associated skills

Turning the tables: Authoring as an asset rather than a burden

We argue that authoring of Intelligent Tutoring Systems can be beneficial for instructors that choose to author content, rather than a time-consuming burden as it is often seen. In order to make this a reality, the authoring process must be easy to understand, must provide immediate benefit to the instructor doing the authoring, and must allow for incremental development and improvement. We describe a methodology that meets all of these needs using concept maps as a basis for authoring. The methodology creates a basis for intelligent support that helps authors improve their course organization and content as they work on the authoring task. We also present details of the rapid prototype being developed to apply the methodology and the initial experiences from its use

The impact of integrated coaching and collaboration within an inquiry learning environment

This thesis explores the design and evaluation of a collaborative, inquiry learning Intelligent Tutoring System for ill-defined problem spaces. The common ground in the fields of Artificial Intelligence in Education and Computer-Supported Collaborative Learning is investigated to identify ways in which tutoring systems can employ both automated coaching and collaborative techniques to support students as they learn. The resulting system, Rashi, offers feedback on student work by using an Expert Knowledge Base to recognize students\u27 work. Evaluation in actual classrooms demonstrated that collaboration significantly improves students\u27 contributions, and some evidence suggests that there is a significant positive correlation between the amount of coaching received and metrics that represent positive inquiry behavior. Finally, this thesis highlights the potential for combining coaching and collaboration such that 1) collaborative work can create more opportunity to provide automated coaching and 2) automated coaching can identify key moments when collaboration should be encouraged

Automated Assessment of Students\u27 Conceptual Understanding: Supporting Students and Teachers Using Data from an Interactive Textbook

Online, interactive textbooks utilizing multimedia are continually gaining popularity in computer science courses. We present a system for automated analysis that can harness the power of these textbook and practice systems to provide information about high-level conceptual understanding to educators. The system presents a visualization using data logged by an interactive textbook to provide numeric estimates of a student\u27s knowledge of course concepts. This information can be used to support teachers and individual students. The basis of our system is a Concept Graph, an artifact representing the concepts to be taught during the course, and their interrelations. We describe the manner in which our system uses these Concept Graphs to provide useful information to educators and students

Memory diagrams: A consistant approach across concepts and languages

Hand-drawn memory diagrams are frequently used in com-puter science to demonstrate new programming concepts and support students\u27 understanding of program function-Ality. These diagrams often vary among courses, instruc-Tors, and languages, which confuse students moving through the curriculum. Consistent memory diagrams throughout a curriculum not only alleviate confusion but ofer a scaffold for students to transfer their understanding between courses taught at different levels of complexity and in different lan-guages. We describe our standardized system for memory diagrams as it is used in our curriculum to demonstrate this scaffolding process through multiple concepts and program-ming languages

TECMap: Technology-Enhanced Concept Mapping for Curriculum Organization and Intelligent Support

© 2019, Springer Nature Switzerland AG. This paper extends a previous publication describing a system that utilizes a wide variety of available assessment information to automatically analyze students’ understanding at a conceptual level and offer relevant automated support to teachers and students. This organization and support can be at the course level or at the level of curriculum for an entire program of study. Intelligent support includes interactive visualization of the conceptual knowledge assessment, individualized suggestions for resources, and suggestions for student groups based on conceptual knowledge assessment. This system differs from prior related work in that it can operate on entire program curricula, and that the basis for analysis and feedback is entirely customized to the individual instructors’ course content. We discuss how the system is configured for courses and the curriculum levels, and describe our experience that indicates the benefits of the approach. We then provide detailed descriptions of how the system performs analysis and offers support in both course and curriculum scenarios

Improving course content while creating customized assessment and support at the conceptual level

We present a system that utilizes a wide variety of available assessment information to automatically analyze students\u27 understanding at a conceptual level and offer relevant automated support to teachers and students. This support includes interactive visualization of the conceptual knowledge assessment, individualized suggestions for resources to improve areas of weakness, and suggestions for dynamic student groups for inclass activities. This system differs from prior related work in that the basis for analysis and feedback is entirely customized to the individual instructors\u27 course content. We discuss how the system is configured for each course, and provide evidence that this configuration process helps instructors improve their course content. We then provide detailed descriptions of how the system performs analysis and offers support including suggesting resources for students and creating dynamic groups within a class. Finally, we discuss the potential benefits provided by this system and how the system is being applied to six different computer science courses currently

Who needs help? Automating student assessment within exploratory learning environments

This article describes efforts to offer automated assessment of students within an exploratory learning environment. We present a regression model that estimates student assessments in an ill-defined medical diagnosis tutor called Rashi. We were pleased to find that basic features of a student’s solution predicted expert assessment well, particularly when detecting low-achieving students. We also discuss how expert knowledge bases might be leveraged to improve this process. We suggest that developers of exploratory learning environments can leverage this technique with relatively few extensions to a mature system. Finally, we describe the potential to utilize this information to direct teachers’ attention towards students in need of help