In this paper, we show how to make a cognitive tutoring agent capable of precise causal reasoning by integrating constraints with data mining algorithms. Putting constraints on recorded interactions between the agent and learners during learning activities allows data mining algorithms to extract the causes of the learners’ problems. Subsequently, the agent uses this information to provide useful and customized explanations to learners.

The track on applied natural language processing is a forum for researchers working in natural language processing (NLP), computational linguistics (CL), and related areas. The rapid pace of development of online materials, most of them in textual form or text combined with other media, has led to a revived interest for tools capable of understanding, organizing and mining those materials. Novel human-computer interfaces (such as talking heads), can benefit from language understanding and generation techniques. Dialoguebased intelligent tutoring systems require advanced dialogue processing, language understanding and generation components in order to assess students' natural language inputs and provide appropriate feedback. Moreover, language can facilitate human-computer interaction for the handicapped (no typing needed) and elderly leading to an ever increasing user base for computer systems.

The plurality of taxonomies, the group of researchers have attempted to make ITS differences amongst available features, and the techniques interactions more naturalistic and conversational. In order used have yielded a variety of approaches. Verbee et al. to accomplish this goal, researchers have analyzed corpora (2006) examined the features used by 16 dialogue act of human-human tutorial dialogues to better understand tagging studies and identified 24 features that have been both individual dialogue acts and patterns of acts that occur previously used. While an extensive discussion of these in human tutoring (Graesser & Person, 1994; Graesser, features is outside the scope of the present paper, the Person, & Magliano, 1995; Litman & Forbes-Riley, 2006; features fall loosely into four categories: word based (e.g.

We revisit a framework for designing peer-based intelligent tutoring systems motivated by McCalla's ecological approach, where learning is facilitated by the previous experiences of peers with a corpus of learning objects. Prior research demonstrated the value of a proposed algorithm for modeling student learning and for selecting the most beneficial learning objects to present to new students. In this paper, we first adjust the validation of this approach to demonstrate its ability to cope with errors in assessing the learning of student peers. We then deepen the representation of learning objects to reflect the expected time to completion and demonstrate how this may lead to more effective selection of learning objects for students, and thus more effective learning. As part of our exploration of these new adjustments, we offer insights into how the size of learning object repositories may affect student learning, suggesting future extensions for the model and its validation.

Although freewriting strategies are commonly taught in composition courses, there have been few empirical studies on freewriting. We address this gap by examining effects of prior writing skills (as measured by a pre-write essay), freewriting training, text-box size (1, 10, 20 lines), and repetitive writing on freewriting quality. Participants watched an agent-based vicarious learning freewriting instruction video or a control video including brief instructions on freewriting. After training, participants wrote six freewrites, two in each box size. Lesson delivery and text box size did not affect expert human ratings of the freewrites. Furthermore, participants did not benefit from writing successive freewrites regardless of their initial skill level. We describe how these results have been used to inform the design of Writing-Pal, an essay-writing intelligent tutoring system.

iSTART is an intelligent tutoring system (ITS) designed to improve students’ reading comprehension. Previous studies have indicated that iSTART is successful; however, these studies have also indicated that students benefit most from long-term interactions that can become tedious and boring. A new game-based version of the system has been developed, called iSTART-ME (motivationally enhanced). Initial results from a usability study with iSTART-ME indicate that this system increases engagement and decreases boredom over time.

Officers of  FLAIRS in 2011.

Natural language processing and statistical methods were used to identify linguistic features associated with the quality of student-generated paragraphs. Linguistic features were assessed using Coh-Metrix. The resulting computational models demonstrated small to medium effect sizes for predicting paragraph quality: introduction quality r2 = .25, body quality r2 = .10, and conclusion quality r2 = .11. Although the variance explained was somewhat low, the linguistic features identified were consistent with the rhetorical goals of paragraph types. Avenues for bolstering this approach by considering individual writing styles and techniques are considered.

In this work we hypothesize that giving students a reflective reading after spoken dialog tutoring in qualitative physics will improve learning. The reading is designed to help students compare similar aspects of previously tutored problems, and to abstract their commonalities. We also hypothesize that student motivation will affect how well the text is processed, and so influence learning. We find that the beneficial effects of the reflective text significantly interact with motivation, such that moderately motivated students learn significantly more from the reflective text than from a non-reflective control text. More poorly or highly motivated students did not benefit from reflective text. These results demonstrate that implicit reflection can improve learning after dialog tutoring with a qualitative physics tutor. They further demonstrate that this result can be obtained with a reflective/abstractive text without recourse to dialog, and that the effectiveness of the text is sensitive to the motivation level of the student.

In this paper, we present a Personalized Intelligent Tutoring System that uses Reinforcement Learning techniques to implicitly learn teaching rules and provide instructions to students based on their needs. The system works on coarsely labeled data with minimum expert knowledge to ease extension to newer domains.