G. Tanner Jackson

Assessing Science Inquiry Skills Using Trialogues

Trialogue-based tasks can be used to gather evidence that may be difficult to obtain using traditional assessment approaches, such as embedded questions. However, more research needs to be done in order to create valid, fair, and reliable conversation tasks that can be used for assessment purposes. This paper describes ongoing efforts at developing and evaluating trialogues for assessing students’ science inquiry skills.

Game-Based Practice in a Reading Strategy Tutoring System: Showdown in iSTART-ME

Many contend that the future of affordable, high-quality education lies in harnessing the potential of computer technologies. While implementing computer technologies in schools has had both failings and challenges (Dynarski et al., 2007), significant progress in the quality of education to some extent depends on our ability to leverage the many advantages of computer technologies. Computer technologies enable adaptive, one-on-one tutoring to virtually all students in the classroom. The most common goal of these one-on-one intelligent tutoring systems (ITSs) is to produce learning gains. Two of the most common areas of learning address content within specific domains (e.g., physics) or cognitive skill acquisition (e.g., strategies to improve reading comprehension). Both types of learning are often characterized by exposure to declarative information and subsequent interaction with the material (Anderson, 1982). However, acquiring a new skill usually requires a significant commitment to continued practice and application. Skills are often developed and improved with practice over an extended period of time (Newell & Rosenbloom, 1981).

Aplicaciones del diálogo humano de tutoría al AutoTutor: Un sistema inteligente de tutoría

At the University of Memphis we have created an intelligent tutoring system, called AutoTutor, that helps students learn by holding a conversation in natural language. Decades of research on human tutoring have guided our creation of AutoTutor, which implements effective tutoring strategies. Several studies have shown that AutoTutor promotes significant learning gains. The current research examines which features of the dialog can account for the learning gains, and assesses AutoTutor?s appropriate use of dialog. Specifically, we explored the dialog patterns from natural tutoring interactions with AutoTutor and analyzed how short pedagogical feedback is related to learning. We found that AutoTutor creates an appropriate model of student knowledge and responds to the students in a manner consistent with their overall performance. These results together with previous findings support the conclusion that AutoTutor is an effective intelligent tutoring system that uses pedagogical strategies that are appropriate for individual learners.

The Impact of Conversational Navigational Guides on the Learning, Use, and Perceptions of Users of a Web Site

Knowledge management systems will presumably benefit from intelligent interfaces, including those with animated conversational agents. One of the functions of an animated conversational agent is to serve as a navigational guide that nudges the user how to use the interface in a productive way. This is a different function from delivering the content of the material. We conducted a study on college students who used a web facility in one of four navigational guide conditions: Full Guide (speech and face), Voice Guide, Print Guide, and No Guide. The web site was the Human Use Regulatory Affairs Advisor (HURAA), a web-based facility that provides help and training on research ethics, based on documents and regulations in United States Federal agencies. The college students used HURAA to complete a number of learning modules and document retrieval tasks. There was no significant facilitation of any of the guides on several measures of learning and performance, compared with the No Guide condition. This result suggests that the potential benefits of conversational guides are not ubiquitous, but they may save time and increase learning under specific conditions that are yet to be isolated.

The Impact of Why/AutoTutor on Learning and Retention of Conceptual Physics

Why/AutoTutor is an intelligent tutoring system for conceptual physics that guides learning through tutorial dialog in natural language. It adapts to student contributions within dialog turns in both a conversationally appropriate and pedagogically effective manner. It uses an animated agent with synthesized speech to engage the student and provide a human-like conversational partner. Why/AutoTutor serves as a learning scaffold throughout the tutoring session and facilitates active knowledge construction on the part of the student. Why/AutoTutor has recently been compared with an ideal information delivery system in order to assess differences in learning gains, the factors that contribute to those gains, and the retention of that knowledge.

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

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An Application of Exploratory Data Analysis in the Development of Game-Based Assessments

While the richness of data from games holds promise for making inferences about players’ knowledge, skills, and attributes (KSAs), standard methods for scoring and analysis do not exist. A key to serious game analytics that measure player KSAs is the identification of player actions that can serve as evidence in scoring models. While game-based assessments may be designed with hypotheses about this evidence, the open nature of game play requires exploration of records of player actions to understand the data obtained and to generate new hypotheses. This chapter demonstrates the use of the 4R’s of Exploratory Data Analysis (EDA): revelation, resistance, re-expression, and residuals to gain close familiarity with data, avoid being fooled, and uncover unexpected patterns. The interactive and iterative nature of EDA allows for the generation of hypotheses about the processes that generated the observed data. Through this framework, possible evidence pieces emerge and the chapter concludes with an explanation of how these can be combined in a measurement model using Bayesian Networks.

The Evolution of an Automated Reading Strategy Tutor: From the Classroom to a Game-Enhanced Automated System

The implementation of effective pedagogical software is difficult to achieve. In this chapter we describe one possible solution to this problem, the evolutionary development of an Intelligent Tutoring System (ITS). This development process typically involves establishing training practices, developing automated instruction, and then amending motivational elements. While this development cycle can take years for completion because each step requires an iterative process of both execution and evaluation, it also has a greater chance of success. We illustrate such a cycle in this chapter in the evolution of an intelligent tutoring and gaming environment [i.e., interactive Strategy Trainer for Active Reading and Thinking-Motivationally Enhanced (iSTART-ME)] from an ITS (i.e., iSTART), which was originally conceived and tested as a human-delivered intervention (i.e., SERT).

Measuring Argumentation Skills with Game-Based Assessments: Evidence for Incremental Validity and Learning

Cognitive scientists and assessment developers have long been concerned with creating comprehensive, authentic measures–especially which elicit evidence of proficiency on one or more constructs under conditions of focus and engagement of test takers reflecting their true performance level. This challenge is particularly arduous for complex constructs, including 21st century skills, that can be highly contextualized and involve the interplay of multiple skills. The current work describes the recent development and evaluation of a game-based assessment on argumentation skills, called Mars Generation One (MGO). Our results show that the in-game process data can substantially improve the measurement of argumentation compared to non-interactive multiple-choice tests. Lastly, students’ show high levels of engagement and improve their argumentation skills during gameplay.

Expectations of Technology: A Factor to Consider in Game-Based Learning Environments

This study investigates how students’ prior expectations of technology affect overall learning outcomes across two adaptive systems, one game-based (iSTART-ME) and one non-game based (iSTART-Regular). The current study (n=83) is part of a larger study (n=124) intended to teach reading comprehension strategies to high school students. Results revealed that students’ prior expectations impacted learning outcomes, but only for students who had engaged in the game-based system. Students who reported positive expectations of computer helpfulness at pretest showed significantly higher learning outcomes in the game-based system compared to students who had low expectations of computer helpfulness. The authors discuss how the incorporation of game-based features in an adaptive system may negatively impact the learning outcomes of students with low technology expectations.