Phoster

Research and Development

The Modeling of Exercises and Activities

Introduction

Improving the modeling of exercises and activities is an important task, one which advances the theory and practice of intelligent tutoring systems. The variety of modeling discussed, herein, is domain-independent, spanning multiple courses and the development of cognition. The proper, rigorous modeling of exercises and activities is non-trivial, an inherently complex task; doing so involves the modeling of cognition, modeling cognitive processes for numerous, relevant paths of progression through the exercises or activities.

Repositories of Exercises and Activities

With properly modeled exercises and activities, possibly a standardized such modeling, intelligent tutoring systems could be described as querying repositories of modeled exercises and activities, modifying existing exercises and activities or generating new exercises and activities, as needed, and loading and interactively presenting sequences of such exercises and activities to students. Essentially, case-based reasoning.

Repositories of exercises and activities require a querying language, possibly a standardized such language. Such querying languages depend strongly upon the modeling of exercises and activities.

Repositories of exercises and activities are more than case-bases, in a number of ways, and require an update language with expressiveness for educational data mining, for providing educational-scientific measurements pertaining to the utility and quality of exercises and activities.

Repositories of exercises and activities are envisioned as interoperable with numerous intelligent tutoring systems and future versions thereof. The modeling of exercises and activities, then, must encompass the specific features and approaches of each contemporary intelligent tutoring system.

Automatic Generation of Exercises and Activities

A number of approaches to the automatic generation of exercises and activities can be interoperable with repository architectures. While repositories may interface as collections of existing exercises and activities, some may, in response to some queries, generate new exercises and activities on the fly.

Learning Objects and Digital Textbooks

Learning objects and digital textbooks also contain exercises and activities. Software components can encapsulate learning objects and digital textbooks, or collections thereof, implementing the interfaces of repositories. That is, intelligent tutoring systems can utilize specific exercises and activities, e.g. from syllabi or course materials, and intelligent tutoring systems can query collections of learning objects and digital textbooks for exercises and activities.

Planning and Generating Sequences of Exercises for the Assessment and Development of Cognition

Developmental Cognitive Neuroscience, Educational Neuroscience and Cognitive Modeling

Developmental cognitive neuroscience, educational neuroscience and cognitive modeling will continue to advance and curricula should expand to include computer-mediated exercises and activities which are designed to activate, strengthen, coordinate and integrate specific regions and processes of the developing human brain.

Automatic Exercise Generation and Sequencing

Exercises and activities for cognitive development and enrichment can be generated by computer technology. Both human- and machine-generated exercises can be sequenced and scheduled by computer technology.

Exercise Variety and Student Engagement

A focus on cognitive development results in a variety of exercises and activities. Variety also arises from mixing exercises and activities from multiple courses during coursework. Variety and the strategic sequencing and scheduling of exercises and activities promote student engagement.

Affect and Student Engagement

Student affect should be monitored during the performance of exercises and activities. So doing facilitates the strategic sequencing and scheduling of exercises and activities. Sequencing and scheduling can be responsive to student affect; for example, scheduling categories of exercises known to be enjoyable to specific students to elevate mood.

Cognitive Load

Cognitive load refers to the total amount of effort being used in the working memory. Advancements to student modeling and cognitive modeling will facilitate better estimation of cognitive load during the performance of exercises and activities.

Multitasking and Task Switching

Discussion of the topics of variety in the sequencing and scheduling of exercises and activities entails a consideration of multitasking and task switching during problem solving. Modeling multitasking and task switching facilitates smoother experiences during and transitions between groups of exercises and activities.

Transfer of Learning

Transfer of learning refers to how learning resulting from one category of exercise or activity effects performance in others. As developmental cognitive neuroscience, educational neuroscience and cognitive modeling continue to advance, so too will our knowledge of transfer of learning and the cognitive neuroscience thereof.

Preparing Conceptual and Procedural Knowledge and Proficiency in Advance

The objectives of longer-scale exercise and activity sequencing and scheduling include preparing students for coursework days, weeks, months or years in advance. Students can, for example, enjoy: exercises and activities with boxes for arithmetical values, preparing them for eventual curricular topics from algebra; exercises and activities involving 3D visuospatial reasoning, preparing them for eventual curricular topics from trigonometry and geometry; and various exercises and activities designed to introduce concepts from calculus.

The Advancement of Intelligent Tutoring Systems

Introduction

Intelligent tutoring systems facilitate computer-aided coursework, computer-mediated exercises and activities, providing mixed-initiative tutorial dialogue, explanation, hints and encouragement.

Intelligent tutoring systems can be advanced in a number of ways.

Intelligent Tutoring at Scale

Intelligent tutoring at scale involves the tutoring of large populations of students. Interesting areas of research and development include: learning from interactions with students to improve the quality of tutoring, educational experimentation, A/B testing and multivariate testing.

Interoperability

Intelligent tutoring systems can be more interoperable with learning objects, digital textbooks and courseware. Intelligent tutoring systems could, for example, utilize exercises and activities from learning objects, digital textbooks and courseware.

Integrating Multiple Domains

The integration of multiple domains facilitates the mixing and scheduling of exercises and activities from multiple courses.

Tutoring Across Schoolyears

Tutoring across schoolyears involves longer-term student modeling and longer-term planning and scheduling of exercises and activities.

Cognitive Development

Intelligent tutoring systems can generate and sequence exercises and activities for purposes of activating, strengthening, coordinating and integrating specific regions and processes of the developing human brain.

Collaborative and Group Learning

Intelligent tutoring systems can tutor groups of students simultaneously.