Pure perceptual-based sequence learning.

Learning a sequence of target locations when the sequence is uncorrelated with a sequence of responses and target location is not the response dimension (pure perceptual-based sequence learning) was examined. Using probabilistic sequences of target locations, the author shows that such learning can be implicit, is unaffected by distance between target locations, and is mostly limited to first-order transition probabilities. Moreover, the mechanism underlying learning affords processing of information at anticipated target locations and appears to be attention based. Implications for hypotheses of implicit sequence learning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Adoption of Technology-Mediated Distance Education: A longitudinal analysis

We investigated the factors that facilitated the fast adoption and utilization of Technology-Mediated Distance Education (TMDE) among higher education institutions. Our analysis was based on a rich data set on the utilization of TMDE between the 1997–1998 and 2000–2001 academic years. The analysis showed that size, public/private status, and location significantly predicted its actual adoption. Being in an urban location negatively affected enrollment in the courses at the undergraduate but not at the graduate level. While the intent to adopt TMDE correlated significantly with actual adoption, many schools that were not interested in TMDE in 1997–1998 adopted it by 2000–2001. Interestingly, late adopters utilized certain technologies as frequently as early adopters, such as synchronous Internet-based instruction and the use of CD-ROMs.     

A visual tool for computer supported learning: The robot motion planning example

We introduce an effective computer aided learning visual tool (CALVT) to teach graph-based applications. We present the robot motion planning problem as an example of such applications. The proposed tool can be used to simulate and/or further to implement practical systems in different areas of computer science such as graphics, computational geometry, robotics and networking. In the robot motion planning example, CALVT enables users to setup the working environment by creating obstacles and a robot of different shapes, specifying starting and goal positions, and setting other path or environment parameters from a user-friendly interface. The path planning system involves several phases. Each of these modules is complex and therefore we provide the possibility of visualizing graphically the output of each phase. Based on our experience, this tool has been an effective one in classroom teaching. It not only cuts down, significantly, on the instructor’s time and effort but also motivates senior/graduate students to pursue work in this specific area of research.     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.     

Learning and Revising User Profiles: The Identification of Interesting Web Sites

We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites. Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental evaluation we compare the Bayesian classifier to computationally more intensive alternatives, and show that it performs at least as well as these approaches throughout a range of different domains. In addition, we empirically analyze the effects of providing the classifier with background knowledge in form of user defined profiles and examine the use of lexical knowledge for feature selection. We find that both approaches can substantially increase the prediction accuracy.     

The impact of presentation graphics on students’ experience in the classroom

To investigate the benefits and perceived effectiveness of instructional technology, students enrolled in several courses were compared on student evaluations of instruction, grades and an attitudinal questionnaire. The instructors of the courses taught the same course across two successive semesters, using traditional “chalk-and-talk” methods the first semester, and PowerPoint the next; all other techniques (e.g., exams, lecture material) were held constant. Results suggest that organization and clarity, entertainment and interest, professor likeability, and good professor behaviors were enhanced with PowerPoint although final grades were not.     

Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system

This paper describes how SWoRD (scaffolded writing and rewriting in the discipline), a web-based reciprocal peer review system, supports writing practice, particularly for large content courses in which writing is considered critical but not feasibly included. To help students gain content knowledge as well as writing and reviewing skills, SWoRD supports the whole cycle of writing, reviews, back-reviews, and rewriting by scaffolding the journal publication process as its authentic practice model. In addition, SWoRD includes algorithms that compute individual reviewer’s review accuracy, which is in turn used to support the various drawbacks of reciprocal peer reviews (e.g., variation in motivation or ability of reviewers). Finally, this paper describes an empirical evaluation showing that the SWoRD approach is effective in improving writing quality in content classes.     

Object Retrieval in the 1st Year of Life: Learning Effects of Task Exposure and Box Transparency.

Before 12 months of age, infants have difficulties coordinating and sequencing their movements to retrieve an object concealed in a box. This study examined (a) whether young infants can discover effective retrieval solutions and consolidate movement coordination earlier if exposed regularly to such a task and (b) whether different environments, indexed by box transparency, would impact the rate of learning and time of discovery of these solutions. Infants (N=12) were presented with an object retrieval task every week from 6 1/2 months of age until they were able to retrieve the toy from the box using coordinated two-handed patterns for 3 weeks. To reach that criterion, infants tested with an opaque box took 2 1/2 months and infants tested with a semitransparent box took 1 1/2 months. Both groups outperformed age-matched controls who received a one-time exposure to the task. Repeated exposure to the task and vision of the toy significantly enhanced this process of solution discovery. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Improved Estimates for the Accuracy of Small Disjuncts

Learning systems often describe a target class as a disjunction of conjunctions of conditions. Recent work has noted that small disjuncts, i.e., those supported by few training examples, typically have poor predictive accuracy. One model of this accuracy is provided by the Bayes-Laplace formula based on the number of training examples covered by the disjunct and the number of them belonging to the target class. However, experiments show that small disjuncts associated with target classes of different relative frequencies tend to have different error rates. This note defines the context of a disjunct as the set of training examples that fail to satisfy at most one of its conditions. An empirical adaptation of the Bayes-Laplace formula is presented that also makes use of the relative frequency of the target class in this context. Trials are reported comparing the performance of the original formula and the adaptation in six learning tasks.     

Role of the Basal Ganglia in Category Learning: How Do Patients With Parkinson's Disease Learn?

The purpose of the present study was to gain a deeper understanding of the role of the basal ganglia in learning and memory by examining learning strategies among patients with basal ganglia dysfunction. Using a probabilistic category learning task (the "weather prediction" task) previously shown to be sensitive to basal ganglia function, the authors examined patterns of performance during learning and used mathematical models to capture different learning strategies. Results showed that patients with Parkinson's disease exhibit different patterns of strategy use. Specifically, most controls initially used a simple, but suboptimal, strategy that focused on single-cue-outcome associations; eventually, however, most controls adopted a more complex, optimal learning strategy, integrating single-cue associations to predict outcomes for multiple-cue stimuli. In contrast, the majority of individuals with Parkinson's disease continued to rely on simple single-cue learning strategies throughout the experiment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)