Which words are hard for autistic children to learn?

Motivated by accounts of concept use in autistic spectrum disorder (ASD), and a computational model of weak central coherence (O’Loughlin & Thagard, 2000) we examined comprehension and production vocabulary in typically-developing children, and those with ASD and Down syndrome (DS). Controlling for frequency, familiarity, length, and imageability, Colorado Meaningfulness played a hitherto unremarked role in the vocabularies of children with ASD. High Colorado Meaningful words were underrepresented in the comprehension vocabularies of 2- to 12-year-olds with ASD. The Colorado Meaningfulness of a word is a measure of how many words can be associated with it. Situations in which high Colorado Meaningfulness words are encountered are typically highly variable, and words with High Colorado Meaningfulness often involve extensive use of context. Our data suggest that the number of contexts in which a particular word can appear has a role in determining vocabulary in ASD. This suggestion is consistent with the weak central coherence theory of autism.

Why children differ in motivation to learn: insights from 13,000 twins from 6 countries

Little is known about why people differ in their levels of academic motivation. This study explored the etiology of individual differences in enjoyment and self-perceived ability for several school subjects in nearly 13,000 twins aged 9 to 16 from 6 countries. The results showed a striking consistency across ages, school subjects, and cultures. Contrary to common belief, enjoyment of learning and children’s perceptions of their competence were no less heritable than cognitive ability. Genetic factors explained approximately 40% of the variance and all of the observed twins’ similarity in academic motivation. Shared environmental factors, such as home or classroom, did not contribute to the twin’s similarity in academic motivation. Environmental influences stemmed entirely from individual specific experiences.

Can a computerised training paradigm assist people with intellectual disabilities to learn cognitive mediation skills? A randomised experiment

Aims: The aim was to examine whether specific skills required for cognitive behavioural therapy (CBT) could be taught using a computerised training paradigm with people who have intellectual disabilities (IDs). Training aimed to improve: a) ability to link pairs of situations and mediating beliefs to emotions, and b) ability to link pairs of situations and emotions to mediating beliefs. Method: Using a single-blind mixed experimental design, sixty-five participants with IDs were randomised to receive either computerised training or an attention-control condition. Cognitive mediation skills were assessed before and after training. Results: Participants who received training were significantly better at selecting appropriate emotions within situation beliefs pairs, controlling for baseline scores and IQ. Despite significant improvements in the ability of those who received training to correctly select intermediating beliefs for situation-feelings pairings, no between-group differences were observed at post-test. Conclusions: The findings indicated that computerised training led to a significant improvement in some aspects of cognitive mediation for people with IDs, but whether this has a positive effect upon outcome from therapy is yet to be established. (C) 2015 Elsevier Ltd. All rights reserved.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

Do firms always want to learn from corporate venture capital investments?

In this paper, the learning intentions and outcomes for corporate venture capital are questioned. Through qualitative research in the oil and gas sector, we identified a desire to control the direction and pace of innovation as the main driver for this type of investments. A new model and framework for CVC are presented. Contrary to the traditional model of CVC, which features a dyadic relation between corporate investor and venture entrepreneur, our model shows that CVC investments create a more complex conjoint of relations between multiple stakeholders. These relations challenge the neo-Schumpeterian model of competition. Using the grounded theory approach, we created a theoretical framework explaining and predicting outcomes of corporate venture capital other than learning. At firm level, our framework conceptualizes CVC programs as dynamic capabilities, and suggests a competitive advantage for the corporate investor through its ability to faster and better integrate the new technology. At market level, we proposed that CVC investments positively affect the pace of innovation in the market through an increased speed of acceptance of technologies supported by corporate investors.

Using Blackboard Administered Precourse and Post course Tests to Assess Student Readiness to Learn and Change in Knowledge

Students arrive at classes with a varying social situations and course subject knowledge. Blackboard is a web based course delivery program that permits testing of students before arriving at the first class. A pretest was used to assess preexisting subject knowledge(S) and a survey was used to assess non-subject (N) factors that might impact the student’s final grade. A posttest was administered after all content was delivered and used to access change in S. [See PDF for complete abstract]

Handy or practical student. Objective: to pass or to learn

With the introduction of the European Higher Education Area and the development of the "Bologna" method in learning certain technological subjects, a pilot assessment procedure was launched in the "old" plan to observe, monitor and analyze the acquiring knowledge of senior students in various academic courses. This paper is a reflection on culture and knowledge. Will students accommodate to get a lower score on tests because they know they have a lot of tooltips to achieve their objectives?. Are their skills lower for these reason?.

EmotiBlog: a model to learn subjective information detection in the new textual genres of the Web 2.0 -a multilingual and multi-genre approach-

Tesis doctoral con mención europea en procesamiento del lenguaje natural realizada en la Universidad de Alicante por Ester Boldrini bajo la dirección del Dr. Patricio Martínez-Barco. El acto de defensa de la tesis tuvo lugar en la Universidad de Alicante el 23 de enero de 2012 ante el tribunal formado por los doctores Manuel Palomar (Universidad de Alicante), Dr. Paloma Moreda (UA), Dr. Mariona Taulé (Universidad de Barcelona), Dr. Horacio Saggion (Universitat Pompeu Fabra) y Dr. Mike Thelwall (University of Wolverhampton). Calificación: Sobresaliente Cum Laude por unanimidad.