Whitney L. Cade

Guru: a computer tutor that models expert human tutors

We present Guru, an intelligent tutoring system for high school biology that has conversations with students, gestures and points to virtual instructional materials, and presents exercises for extended practice. Gurus instructional strategies are modeled after expert tutors and focus on brief interactive lectures followed by rounds of scaffolding as well as summarizing, concept mapping, and Cloze tasks. This paper describes the Guru session and presents learning outcomes from an in-school study comparing Guru, human tutoring, and classroom instruction. Results indicated significant learning gains for students in the Guru and human tutoring conditions compared to classroom controls.

What Works: Creating Adaptive and Intelligent Systems for Collaborative Learning Support

An emerging trend in classrooms is the use of collaborative learning environments that promote lively exchanges between learners in order to facilitate learning. This paper explored the possibility of using discourse features to predict student and group performance during collaborative learning interactions. We investigated the linguistic patterns of group chats, within an online collaborative learning exercise, on five discourse dimensions using an automated linguistic facility, Coh-Metrix. The results indicated that students who engaged in deeper cohesive integration and generated more complicated syntactic structures performed significantly better. The overall group level results indicated collaborative groups who engaged in deeper cohesive and expository style interactions performed significantly better on posttests. Although students do not directly express knowledge construction and cognitive processes, our results indicate that these states can be monitored by analyzing language and discourse. Implications are discussed regarding computer supported collaborative learning and ITSs to facilitate productive communication in collaborative learning environments.

Collaborative lecturing by human and computer tutors

We implemented and evaluated a collaborative lecture module in an ITS that models the pedagogical and motivational tactics of expert human tutors Inspired by the lecture delivery styles of the expert tutors, the collaborative lectures of the ITS were conversational and interactive, instead of a polished one-way information delivery from tutor to student We hypothesized that the enhanced interactivity of the expert tutor lectures were linked to efforts to promote student engagement This hypothesis was tested in an experiment that compared the collaborative lecture module (dialogue) to less interactive alternatives such as monologues and vicarious dialogues The results indicated that students in the collaborative lecture condition reported more arousal (a key component of engagement) than the controls and that arousal was positively correlated with learning gains We discuss the implications of our findings for ITSs that aspire to model expert human tutors.

How do they do it? investigating dialogue moves within dialogue modes in expert human tutoring

Expert human tutors are widely considered to be the gold standard for increasing student learning. While not every student has access to an expert tutor, it is possible to model intelligent tutoring systems after expert tutors. In an effort to achieve this goal, we have analyzed a corpus of 50 hours of one-to-one expert human tutoring sessions. This corpus was coded for speech acts (dialogue moves) and larger pedagogical strategies (dialogue modes). Using mixed-effects modeling, we found that expert tutors differentially used dialogue moves depending on the dialogue mode. Specifically, tutor posed questions, explanations, and motivational statements were predictive of different dialogue modes (e.g., Lecture, Scaffolding).

Building rapport with a 3D conversational agent

While embodied conversational agents improve a users experience with a system, systems meant for repeated use may need agents that build a relationship with the user. Anita is a low-cost 3D agent capable of talking, displaying emotions, gesturing, and postural mimicry, all of which may increase the rapport between agent and user. Motion capture and pressure sensors were used to create an agent capable of realistic, responsive motions.

Authoring Intelligent Tutoring Systems Using Human Computation: Designing for Intrinsic Motivation

This paper proposes a methodology for authoring of intelligent tutoring systems using human computation. The methodology embeds authoring tasks in existing educational tasks to avoid the need for monetary authoring incentives. Because not all educational tasks are equally motivating, there is a tension between designing the human computation task to be optimally efficient in the short term and optimally motivating to foster participation in the long term. In order to enhance intrinsic motivation for participation, the methodology proposes designing the interaction to promote user autonomy, competence, and relatedness as defined by Self-Determination Theory. This design has implications for learning during authoring.

Animated Presentation of Pictorial and Concept Map Media in Biology

Intelligent tutoring systems are beginning to include more varied forms of media, but little is known about how to choose the appropriate media and whether or not it should be animated. This study used a 2 animated/static x 2 picture/concept map factorial design in order to evaluate the effect of animation and media type on conceptual knowledge, relational knowledge, and free recall. Learners on Mechanical Turk N = 208 were exposed to one of four conditions in which they viewed a modified Khan Academy video on cell parts. We found that animation induced higher learning gains when it comes to relational knowledge. For conceptual knowledge, animated concept maps outperformed animated pictures while static pictures produced slightly more learning than static concept maps. Our results indicate that using animations to slowly build complexity in visual displays is particularly important when the displays have a rich structure as in concept maps.