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 define sentence generalization and generalization diagrams as a special case of least general generalization (LGG) as applied to linguistic parse trees. Similarity measure between linguistic parse trees is developed as LGG operation on the lists of sub-trees of these trees. The diagrams introduced are representation of mapping between the syntactic generalization level and semantic generalization level. Generalization diagrams are intended as a framework to compute semantic similarity between texts relying on linguistic parse tree data. Such structured approach significantly improves text relevance assessment in a horizontal domain, where ontologies are not available

We consider the problem of finding generalized plans for situations where the number of objects may be unknown and unbounded during planning. The input is a domain specification, a goal condition, and a class of concrete problem instances or initial states to be solved, expressed in an abstract first-order representation. Starting with an empty generalized plan, our overall approach is to incrementally increase the applicability of the plan by identifying a problem instance that it cannot solve, invoking a classical planner to solve that problem, generalizing the obtained solution and merging it back into the generalized plan. The main contributions of this paper are methods for (a) generating and solving small problem instances not yet covered by an existing generalized plan, (b) translating between concrete classical plans and abstract plan representations, and (c) extending partial generalized plans and increasing their applicability. We analyze the theoretical properties of these methods, prove their correctness, and illustrate experimentally their scalability. The resulting hybrid approach shows that solving only a few, small, classical planning problems can be sufficient to produce a generalized plan that applies to infinitely many problems with unknown numbers of objects.

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.

Agreement asymmetries are the most debated issue in Arabic linguistics. Even though the facts suggest a unified treatment based on the properties of agreement, most of the researchers in this field don’t take into account the essential difference between grammatical agreement and anaphoric agreement. We do propose such a distinction to explain these asymmetries and we propose an analysis that we implement in the ACCG framework.

We present a software tool that is able to automatically translate an NP problem into a STRIPS problem such that the former problem has a solution iff the latter has one, a solution for the latter can be transformed into a solution for the former, and all this can be done efficiently. Moreover, the tool is built such that it only produces problems that belong to a fragment of STRIPS that is solvable in non-deterministic polynomial time, a fact that guarantees that the whole approach is not an overkill. This tool has interesting applications. For example, with the advancement of planning technology, it can be used as an off-the-shelf method to solve general NP problems with the help of planners and to automatically generate benchmark problems of known complexity in a systematic and controlled manner. Another interesting contribution is related to the area of Knowledge Engineering in which one of the goals is to devise automatic methods for using the available planning technology to solve real-life problems.

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.

In this paper we introduce a system that is designed to automatically populate a knowledge base from both structured and unstructured text given an ontology. Our system is designed as a modular end-to-end system that takes structured or unstructured data as input, extracts information, maps relevant information to an ontology, and finally disambiguates entities in the knowledge base. The novelty of our approach is that it is domain independent and can easily be adapted to new ontologies and domains. Unlike most knowledge base population systems, ours includes entity detection. This feature allows one to employ very complex ontologies that include events and the entities that are involved in the events.

The results of 2,256 neuroimaging experiments were an- alyzed using singular value decomposition (SVD) and non-negative matrix factorization (NMF) to extract pat- terns in the data. To evaluate the techniques’ efficacy at capturing regularities in the data, one positive and one negative result from each of 100 random experi- ments were treated as missing, and the values were it- eratively reconstructed using each technique for dimen- sionality reduction. Under the best conditions, preci- sion and recall of roughly 78% was achieved for each method. Weighting the domain matrix and area matrix to have equal first eigenvalues before combining them, a technique known as blending, significantly improved re- sults for both methods. While using unnormalized data appeared to produce a peak in results for 10-15 dimen- sions, normalizing to take into account variation in the popularity of experiment types removed the effect. The basis vectors produced by each method do not support the idea that current cognitive ontologies map well to individual brain areas.

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.