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.

In recent years there has been a significant decline in the number of college students choosing majors in computer science or technology related fields. Although this trend is beginning to turn around at the undergraduate level, there remains disparity in the number of under-represented minority students who earn graduate degrees as compared to majority students. Additionally, within the United States, there is an achievement gap between under-represented minority students and majority students at a time when underrepresented groups are becoming an increasing proportion of the national labor force. This reluctance to study Science, Technology, Engineering, and Mathematics (STEM) disciplines must be confronted and changed if the United States is to maintain a competitive position within the global market. Effective use of learning technologies is vital to solving many of our current STEM learning challenges. Robotics is a growing research area in computer science education. We use robotics as a technology tool captivate and engage students in research in robotics.

Determining the position of a robot in an environment, termed localization, is one of the challenges facing roboticist. Localization is essential to solving more complex problems such as locomotion, path planning and environmental learning. Our lab is developing a multi-agent system to use multiple small robots to accomplish tasks normally completed by larger robots. However, because of the reduced size of these robots, methods previously used to determine the position of the robot, such as GPS, cannot be employed. The problem we are facing is that we need to be able to determine the position of each of the robots in this multi-agent system simultaneously. We have developed a system to help track and identify robots using an overhead camera and LEDs, mounted on the robots, to efficiently solve the localization problem.

In this article, a multipopulation Cultural Algorithm (MP-CA) is proposed to solve Job Shop Scheduling Problems (JSSP). The idea of using multiple populations in a Cultural Algorithm is implemented for the first time in JSSP. The proposed method divides the whole population into a number of sub-populations. On each sub-population, a local CA is applied which includes its own population space as well as belief space. The local CAs use Evolutionary Programming (EP) to evolve their populations, and moreover they incorporate a local search approach to speed up their convergence rates. The local CAs communicate with each other using knowledge migration which is a novel concept in CA. The proposed method extracts two types of knowledge including normative and topographic knowledge and uses the extracted knowledge to guide the evolutionary process to generate better solutions. The MP-CA is evaluated using a well-known benchmark. The results show that the MP-CA outperforms some of the existing methods by offering better solutions as well as better convergence rates, and produces competitive solutions when compared to the state-of-the-art methods used to deal with JSSPs.

Gene selection has become a vital component in the learning process when using high-dimensional gene expression data. Although extensive research has been done towards evaluating the performance of classifiers trained with the selected features, the stability of feature ranking techniques has received relatively little study. This work evaluates the robustness of eleven threshold-based feature selection techniques, examining the impact of data sampling and class noise on the stability of feature selection. To assess the robustness of feature selection techniques, we use four groups of gene expression datasets, employ eleven threshold-based feature rankers, and generate artificial class noise to better simulate real-world datasets. The results demonstrate that although no ranker consistently outperforms the others, MI and Dev show the best stability on average, while GI and PR show the least stability on average. Results also show that trying to balance datasets through data sampling has on average no positive impact on the stability of feature ranking techniques applied to those datasets. In addition, increased feature subset sizes improve stability, but only does so reliably for noisy datasets.

The accuracy of an automated reading tutor in tracking the reader’s position is affected by phenomena at the frontier of the speech recognizer’s output as it evolves in real time. We define metrics of real-time tracking accuracy computed from the recognizer’s successive partial hypotheses, in contrast to previous metrics computed from the final hypothesis. We analyze the resulting considerable loss in real-time accuracy, and propose and evaluate a method to address it. Our method raises real-time accuracy from 58% to 70%, which should improve the quality of the tutor’s feedback.

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.

The authors used the computational tool Coh-Metrix to examine expert writers’ paraphrases and in particular, how experts paraphrase text passages using condensing strategies. The overarching goal of this study was to develop machine learning algorithms to aid in the automatic detection of paraphrases and paraphrase types. To this end, three experts were instructed to paraphrase by condensing a set of target passages. The linguistic differences between the original passages and the condensed paraphrases were then analyzed using Coh-Metrix. The condensed paraphrases were accurately distinguished from the original target passages based on the number of words, word frequency, and syntactic complexity.

Protecting our nation’s ports is a critical challenge for homeland security and requires the research, development and deployment of new technologies that will allow for the efficient securing of shipments entering this country. Most approaches look only at statistical irregularities in the attributes of the cargo, and not at the relationships of this cargo to others. However, anomalies detected in these relationships could add to the suspicion of the cargo, and therefore improve the accuracy with which we detect suspicious cargo. This paper proposes an improvement in our ability to detect suspicious cargo bound for the U.S. through a graph-based anomaly detection approach. Using anonymized data received from the Department of Homeland Security, we demonstrate the effectiveness of our approach and its usefulness to a homeland security analyst who is tasked with uncovering illegal and potentially dangerous cargo shipments.

Maritime threat detection is a challenging problem because maritime environments can involve a complex combination of concurrent vessel activities, and only a small fraction of these may be irregular, suspicious, or threatening. Previous work on this task has been limited to analyses of single vessels using simple rule-based models that alert watchstanders when a proximity threshold is breached. We claim that Probabilistic Graphical Models (PGMs) can be used to more effectively model complex maritime situations. In this paper, we study the performance of PGMs for detecting (small boat) maritime attacks. We describe three types of PGMs that vary in their representational expressiveness and evaluate them on a threat recognition task using track data obtained from force protection naval exercises involving unmanned sea surface vehicles. We found that the best-performing PGMs can outperform the deployed rule-based approach on these tasks, though some PGMs require substantial engineering and are computationally expensive.