A school-based cognitive behavior modification intervention strategy for learned helpless mentally retarded students

This study assessed the usefulness of a cognitive behavior modification (CBM) intervention package with mentally retarded students in overcoming learned helplessness and improving learning strategies. It also examined the feasibility of instructing teachers in the use of such a training program for a classroom setting. A modified single subject design across individuals was employed using two groups of three subjects. Three students from each of two segregated schools for the mentally retarded were selected using a teacher questionnaire and pupil checklist of the most learned helpless students enrolled there. Three additional learned helplessness assessments were conducted on each subject before and after the intervention in order to evaluate the usefulness of the program in alleviating learned helplessness. A classroom environment was created with the three students from each school engaged in three twenty minute work sessions a week with the experimenter and a tutor experimenter (TE) as instructors. Baseline measurements were established on seven targeted behaviors for each subject: task-relevant speech, task-irrelevant speech, speech denoting a positive evaluation of performance, speech denoting a negative evaluation of performance, proportion of time on task, non-verbal positive evaluation of performance and non-verbal negative evaluation of performance. The intervention package combined a variety of CBM techniques such as Meichenbaum's (1977) Stop, Look and Listen approach, role rehearsal and feedback. During the intervention each subject met with his TE twice a week for an individual half-hour session and one joint twenty minute session with all three students, the experimentor and one TE. Five weeks after the end of this experiment one follow up probe was conducted. All baseline, post-intervention and probe sessions were videotaped. The seven targeted behaviors were coded and comparisons of baseline, post intervention, and probe testing were presented in graph form. Results showed a reduction in learned helplessness in all subjects. Improvement was noted in each of the seven targeted behaviors for each of the six subjects. This study indicated that mentally retarded children can be taught to reduce learned helplessness with the aid of a CBM intervention package. It also showed that CBM is a viable approach in helping mentally retarded students acquire more effective learning strategies. Because the TEs (Tutor experimenters) had no trouble learning and implementing this program, it was considered feasible for teachers to use similar methods in the classroom.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Predictors of academic achievement and social competence in adolescence : facilitation, inhibition and general cognitive processing /

The relevance of attentional measures to cognitive and social adaptive behaviour was examined in an adolescent sample. Unlike previous research, the influence of both inhibitory and facilitory aspects of attention were studied. In addition, contributions made by these attentional processes were compared with traditional psychometric measures of cognitive functioning. Data were gathered from 36 grade 10 and 1 1 high school students (20 male and 16 female students) with a variety of learning and attentional difficulties. Data collection was conducted in the course of two testing sessions. In the first session, students completed questionnaires regarding their medical history, and everyday behaviours (the Brock Adaptive Functioning Questionnaire), along with non-verbal problem solving tasks and motor speed tasks. In the second session, students performed working memory measures and computer-administered tasks assessing inhibitory and facilitory aspects of attention. Grades and teacher-rated measures of cognitive and social impulsivity were also gathered. Results indicate that attentional control has both cognitive and social/emotional implications. Performance on negative priming and facilitation trials from the Flanker task predicted grades in core courses, social functioning measures, and cognitive and social impulsivity ratings. However, beneficial effects for academic and social functioning associated with inhibition were less prevalent in those demonstrating a greater ability to respond to facilitory cues. There was also some evidence that high levels of facilitation were less beneficial to academic performance, and female students were more likely to exceed optimal levels of facilitory processing. Furthermore, lower negative priming was ''S'K 'i\':y-: -'*' - r " j«v ; ''*.' iij^y Inhibition, Facilitation and Social Competence 3 associated with classroom-rated distraction and hyperactivity, but the relationship between inhibition and social aspects of impulsivity was stronger for adolescents with learning or reading problems, and the relationship between inhibition and cognitive impulsivity was stronger for male students. In most cases, attentional measures were predictive of performance outcomes independent of traditional psychometric measures of cognitive functioning. >,, These findings provide support for neuropsychological models linking inhibition to control of interference and arousal, and emphasize the fundamental role of attention in everyday adolescent activities. The findings also warrant further investigation into the ways which inhibitory and facilitory attentional processes interact, and the contextdependent nature of attentional control.associated with classroom-rated distraction and hyperactivity, but the relationship between inhibition and social aspects of impulsivity was stronger for adolescents with learning or reading problems, and the relationship between inhibition and cognitive impulsivity was stronger for male students. In most cases, attentional measures were predictive of performance outcomes independent of traditional psychometric measures of cognitive functioning. >,, These findings provide support for neuropsychological models linking inhibition to control of interference and arousal, and emphasize the fundamental role of attention in everyday adolescent activities. The findings also warrant further investigation into the ways which inhibitory and facilitory attentional processes interact, and the contextdependent nature of attentional control.

Experiential learning instruments : an examination of the concept of the instrumental approach in experiential learning

Experiential Learning Instruments (ELls) are employed to modify the leamer's apprehension and / or comprehension in experiential learning situations, thereby improving the efficiency and effectiveness of those modalities in the learning process. They involve the learner in reciprocally interactive and determining transactions with his/her environment. Experiential Learning Instruments are used to keep experiential learning a process rather than an object. Their use is aimed at the continual refinement of the learner's knowledge and skill. Learning happens as the leamer's awareness, directed by the use of Ells, comes to experience, monitor and then use experiential feedback from living situations in a way that facilitates knmvledge/skill acquisition, self-correction and refinement. The thesis examined the literature relevant to the establishing of a theoretical experiential learning framework within which ELls can be understood. This framework included the concept that some learnings have intrinsic value-knowledge of necessary information-while others have instrumental value-knowledge of how to learn. The Kolb Learning Cycle and Kolb's six characteristics of experiential learning were used in analyzing three ELls from different fields of learning-saxophone tone production, body building and interpersonal communications. The ELls were examined to determine their learning objectives and how they work using experiential learning situations. It was noted that ELls do not transmit information but assist the learner in attending to and comprehending aspects of personal experience. Their function is to telescope the experiential learning process.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Towards Evaluating and Modelling the Impacts of Mobile-Based Augmented Reality Applications on Learning and Engagement

Mobile augmented reality applications are increasingly utilized as a medium for enhancing learning and engagement in history education. Although these digital devices facilitate learning through immersive and appealing experiences, their design should be driven by theories of learning and instruction. We provide an overview of an evidence-based approach to optimize the development of mobile augmented reality applications that teaches students about history. Our research aims to evaluate and model the impacts of design parameters towards learning and engagement. The research program is interdisciplinary in that we apply techniques derived from design-based experiments and educational data mining. We outline the methodological and analytical techniques as well as discuss the implications of the anticipated findings.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.