Psychosocial assessments and treatments are effective for a range of psychological problems.One particular area of concern is youth suicide. This paper reports on the SAMHT intelligent tutoring system, which provides youth suicide risk assessment training.SAMHT's interactive avatar interface is based on an intelligent backend, and provides a believable interaction that is effective for training mental health professionals.

Reading tutors can provide an unprecedented opportunity to collect and analyze large amounts of data for understanding how students learn. We trained models of oral reading prosody (pitch, intensity, and duration) on a corpus of narrations of 4558 sentences by 11 fluent adults. We used these models to evaluate the oral reading prosody of 85,209 sentences read by 55 children (mostly) 7-10 years old who used Project LISTEN's Reading Tutor during the 2005-2006 school year. We mined the resulting data to pinpoint the specific common syntactic and lexical features of text that children scored best and worst on. These features predict their fluency and comprehension test scores and gains better than previous models. Focusing on these features may help human or automated tutors improve children’s fluency and comprehension more effectively.

Modeling skills are essential during the process of learning programming. ITS systems for modeling are typically hard to build due to the ill-definedness of most modeling tasks. This paper presents a system that can teach UML skills to novice programmers. The system is “simple and cheap” in the sense that it only requires an expert solution against which the student solutions are compared, but still flexible enough to accommodate certain degrees of solution flexibility and variability that are characteristic of modeling tasks. An empirical evaluation via a controlled lab study showed that the system worked fine and, while not leading to significant learning gains as compared to a control condition, still revealed some promising results.

Grimes and Warschauer (2010) describe two accuracy (Warschauer & Ware, 2006), there have been kinds of systems: automated essay scoring (AES) and relatively few evaluations of student improvement (e.g., automated writing evaluation (AWE). AES systems strive Kellogg, Whiteford, & Quinlan, 2010) or the role of to assign accurate and reliable scores to essays or specific feedback (e.g., Roscoe, Varner, Cai, Weston, Crossley, & writing features (e.g., mechanics). Scores are generated McNamara, 2011). Hence, in this paper, we explore and using various artificial intelligence (AI) methods, including describe a method for developing pedagogically-guided statistical modeling, natural language processing (NLP), algorithms that guide formative feedback in an intelligent and Latent Semantic Analysis (LSA) (Shermis & Burstein, tutor system (ITS) for writing.

In the area of student knowledge assessment, knowledge tracing is a model that has been used for over a decade to predict student knowledge and performance. Many modifications to this model have been proposed and evaluated, however, the modifications are often based on a combination of intuition and experience in the domain. This method of model improvement can be difficult for researchers without high level of domain experience and furthermore, the best improvements to the model could be unintuitive ones. Therefore, we propose a completely data driven approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvement. Our results suggest a variety of different improvements to knowledge tracing many of which have not been explored.

Concept maps are frequently used in K-12 educational settings. The purpose of this study is to determine whether students’ performance on interactive concept map tasks in Guru, an intelligent tutoring system, is related to immediate and delayed learning outcomes. Guru is a dialogue-based system for high-school biology that intersperses concept map tasks within the tutorial dialogue. Results indicated that when students first attempt to complete concept maps, time spent on the maps may be a good indicator of their understanding, whereas the errors they make on their second attempts with the maps may be an indicator of the knowledge they are lacking.  This pattern of results was observed for one cycle of testing, but not replicated in a second cycle. Differences in the findings for the two testing cycles are most likely due to topic variations.

One-on-one tutoring is significantly more effective than traditional classroom instruction. In recent years, automated tutoring systems are approaching that level of effectiveness by engaging students in rich natural language dialogue that contributes to learning. A promising approach for further improving the effectiveness of tutorial dialogue systems is to model the differential effectiveness of tutorial strategies, identifying which dialogue moves or combinations of dialogue moves are associated with learning. It is also important to model the ways in which experienced tutors adapt to learner characteristics. This paper takes a corpus- based approach to these modeling tasks, presenting the results of a study in which task-oriented, textual tutorial dialogue was collected from remote one-on-one human tutoring sessions. The data reveal patterns of dialogue moves that are correlated with learning, and can directly inform the design of student-adaptive tutorial dialogue management systems.

Intelligent Tutoring Systems (ITSs) have begun to incorporate game-based components in an attempt to balance the learning benefits of ITSs with the motivational benefits of games. iSTART-ME (Motivationally Enhanced) is a new game-based learning environment that was developed on top of an existing ITS (iSTART). In a multi-session lab-based efficacy study with 125 high school students, those students with a low prior reading ability who were trained by a game-based tutoring system (iSTART-ME) or a traditional intelligent tutoring system (iSTART-Regular) performed significantly better on posttest measures than students assigned to a time-delayed control condition. Additionally, the low reading ability students who interacted with the game-based system had a tendency to gain more than students in the traditional ITS system.

Intelligent tutoring systems research aims to produce systems that meet or exceed the effectiveness of one-on-one expert human tutoring. Theory and empirical study suggest that affective states of the learner must be addressed to achieve this goal. While many affective measures can be utilized, posture offers the advantages of non-intrusiveness and ease of interpretation. This paper presents an accurate posture estimation algorithm applied to a computer-mediated tutoring corpus of depth recordings. Analyses of posture and session-level student reports of engagement and cognitive load identified significant patterns. The results indicate that disengagement and frustration may coincide with closer postural positions and more movement, while focused attention and less frustration occur with more distant, stable postural positions. It is hoped that this work will lead to intelligent tutoring systems that recognize a greater breadth of affective expression through channels of posture and gesture.

We evaluated an affect-sensitive version of AutoTutor, a dialogue based ITS that simulates human tutors. While the original AutoTutor is sensitive to students’ cognitive states, the affect-sensitive tutor (Supportive tutor) also responds to students’ affective states (boredom, confusion, and frustration) with empathetic, encouraging, and motivational dialogue moves that are accompanied by appropriate emotional expressions. We conducted an experiment that compared the Supportive and Regular (non-affective) tutors over two 30-minute learning sessions with respect to perceived effectiveness, fidelity of cognitive and emotional feedback, engagement, and enjoyment. The results indicated that, irrespective of tutor, students’ ratings of engagement, enjoyment, and perceived learning decreased across sessions, but these ratings were not correlated with actual learning gains. In contrast, students’ perceptions of how closely the computer tutors resembled human tutors increased across learning sessions, was related to the quality of tutor feedback, the increase was greater for the Supportive tutor, and was a powerful predictor of learning. Implications of our findings for the design of affect-sensitive ITSs are discussed.