Jianmin Dai

Promoting Metacognitive Awareness within a Game-Based Intelligent Tutoring System

Metacognitive awareness has been shown to be a critical skill for academic success. However, students often struggle to regulate this ability during learning tasks. The current study investigates how features designed to promote metacognitive awareness can be built into the game-based intelligent tutoring system (ITS) iSTART-2. College students (n=28) interacted with iSTART-2 for one hour, completing lesson videos and practice activities. If students’ performance fell below a minimum threshold during game-based practice, they received a pop-up that alerted them of their poor performance and were subsequently transitioned to a remedial activity. Results revealed that students’ scores in the system improved after they were transitioned (even when they did not complete the remedial activity). This suggests that the pop-up feature in iSTART-2 may indirectly promote metacognitive awareness, thus leading to increased performance. These results provide insight into the potential benefits of real-time feedback designed to promote metacognitive awareness within a game-based learning environment.

Teaching iSTART to understand Spanish

iSTART is a web-based reading comprehension tutor. A recent translation of iSTART from English to Spanish has made the system available to a new audience. In this paper, we outline several challenges that arose during the development process, specifically focusing on the algorithms that drive the feedback. Several iSTART activities encourage students to use comprehension strategies to generate self-explanations in response to challenging texts. Unsurprisingly, analyzing responses in a new language required many changes, such as implementing Spanish natural language processing tools and rebuilding lists of regular expressions used to flag responses. We also describe our use of an algorithm inspired from genetics to optimize the Fischer Discriminant Function Analysis coefficients used to determine self-explanation scores.

StairStepper: An Adaptive Remedial iSTART Module

This paper introduces StairStepper, a new addition to Interactive Strategy Training for Active Reading and Thinking (iSTART), an intelligent tutoring system (ITS) that provides adaptive self-explanation training and practice. Whereas iSTART focuses on improving comprehension at levels geared toward answering challenging questions associated with complex texts, StairStepper focuses on improving learners’ performance when reading grade-level expository texts. StairStepper is designed as a scaffolded practice activity wherein text difficulty level and task are adapted according to learners’ performance. This offers a unique module that provides reading comprehension tutoring through a combination of self-explanation practice and answering of multiple-choice questions representative of those found in standardized tests.

Bring It on! Challenges Encountered While Building a Comprehensive Tutoring System Using ReaderBench.

Intelligent Tutoring Systems (ITSs) are aimed at promoting acquisition of knowledge and skills by providing relevant and appropriate feedback during students’ practice activities. ITSs for literacy instruction commonly assess typed responses using Natural Language Processing (NLP) algorithms. One step in this direction often requires building a scoring mechanism that matches human judgments. This paper describes the challenges encountered while implementing an automated evaluation workflow and adopting solutions for increasing performance of the tutoring system. The algorithm described here comprises multiple stages, including initial pre-processing, a rule-based system for pre-classifying self-explanations, followed by classification using a Support Virtual Machine (SVM) learning algorithm. The SVM model hyper-parameters were optimized using grid search approach with 4,109 different self-explanations scored 0 to 3 (i.e., poor to great). The accuracy achieved for the model was 59% (adjacent accuracy = 97%; Kappa = .43).