Matthew E. Jacovina

iSTART-2: A Reading Comprehension and Strategy Instruction Tutor.

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Promoting Metacognition Within a Game-Based Environment

Metacognition refers to students’ ability to reflect upon what they know and what they do not know. However, many students often struggle to master this regulatory skill. We have designed and implemented two features to promote metacognition within the game-based system iSTART-2. These two features have been tested and shown to have positive impacts on students’ ability to reflect upon their performance. Future work is being planned to further explore the most effective way to implement these features and the ultimate impact they have on learning outcomes. We are seeking advice and feedback on the methodology and metacognitive feature design that will be included in a series of follow-up studies. The implications of this work for both iSTART-2 and the AIED field are discussed.

iSTART Therefore I Understand: But Metacognitive Supports Did Not Enhance Comprehension Gains.

iSTART is an intelligent tutoring system designed to provide self-explanation instruction and practice to improve students’ comprehension of complex, challenging text. This study examined the effects of extended game-based practice within the system as well as the effects of two metacognitive supports implemented within this practice. High school students (n = 234) were either assigned to an iSTART treatment condition or a control condition. Within the iSTART condition, students were assigned to a 2 × 2 design in which students provided self-assessments of their performance or were transferred to Coached Practice if their performance did not reach a certain performance threshold. Those receiving iSTART training produced higher self-explanation and inference-based comprehension scores. However, there were no direct effects of either metacognitive support on these learning outcomes.

Am I Wrong or Am I Right? Gains in Monitoring Accuracy in an Intelligent Tutoring System for Writing

We investigated whether students increased their self-assessment accuracy and essay scores over the course of an intervention with a writing strategy intelligent tutoring system, W-Pal. Results indicate that students were able to learn from W-Pal, and that the combination of strategy instruction, game-based practice, and holistic essay-based practice led to equivalent gains in self-assessment accuracy compared to heavier doses of deliberate writing practice (offering twice the amount of system feedback).

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.

Lost in Springdale: An interactive narrative for adult literacy learners

The past years have witnessed an increased use of applied games for developing and evaluating communication skills. These skills benefit from in-terpersonal interactions. Providing feedback to students practicing communica-tion skills is difficult in a traditional class setting with one teacher and many students. This logistic challenge may be partly overcome by providing training using a simulation in which a student practices with communication scenarios. A scenario is a description of a series of interactions, where at each step the player is faced with a choice. We have developed a scenario editor that enables teachers to develop scenarios for practicing communication skills. A teacher can develop a scenario without knowledge of the implementation. This paper presents the implementation architecture for such a scenario-based simulation.This paper presents an initial evaluation of different forms of adaptation based on learning style and knowledge level, which were implemented in an adaptive e-learning system. An experiment conducted in a learning context with 174 participants produced significant results in terms of learning gain. They indicate that adaptation based on both learning style and knowledge level yields significantly better learning gain than adaptation based on learning style only, and better than adaptation based on knowledge level only.Technology Enhanced Learning (TEL) largely focuses on the retrieval and reuse of educational resources from Web platforms like Coursera. Unfortunately, Coursera does not provide educational metadata of its content. To overcome this limitation, this study proposes a data mining approach for discovering Teaching Contexts (TC) where resources have been delivered in. Such TCs can facilitate the retrieval of resources for the teaching preferences and requirements of teachers.Gamification is the use of game design elements in non-game contexts, and it has reported potential benefits for students. However, the proposals supporting teachers to create gamified ubiquitous learning situations are tied to specific activities and enactment technologies. To start addressing this issue, we propose a system to help teachers design and deploy these situations involving a variety of technologies frequently used in education.Adapting tasks to learner characteristics is essential when selecting appropriate tasks for learners [5]. This paper investigates how humans adapt exercise selection to learner self-esteem (SE) and performance, to allow a future Intelligent Tutoring System (ITS) to use these adaptations. Self esteem is an important factor in learning as it is a significant predictor of academic performance [4]. Previous research adapts task selection to other characteristics e.g. past performance [1], but little work focuses on task selection based on learner personality.

Game Features and Individual Differences: Interactive Effects on Motivation and Performance

To optimize the benefits of game-based practice within Intelligent Tutoring Systems (ITSs), researchers examine how game features influence students’ motivation and performance. The current study examined the influence of game features and individual differences (reading ability and learning intentions) on motivation and performance. Participants (n = 58) viewed lesson videos in iSTART-2, an ITS designed to improve reading comprehension skills, and practiced with either a game-like activity or a minimally game-like activity. No main effects of game environment were observed. However, there was an interaction between game environment and pretest learning intentions in predicting students’ self-reported effort. The correlation between learning intentions and self-reported effort was not significant for students who practiced with the more game-like activity, whereas it was for students who practiced in the less game-like activity. We discuss the implications for this interaction and how it might drive future research.