The California Test of Mental Maturity - Long Form Level 2 and the Iowa-Brace Test of Motor Educability were administered to 112 girls from grade five classes in seven randomly selected city schools of Vancouver, British Columbia. The scores obtained on both tests were slightly higher than those expected in the normal population. A small positive relationship (0.30) was found between total Iowa-Brace scores and total IQ. The correlation of the Non-Language Section IQ of the California Test of Mental Maturity with Iowa-Brace total score was significantly (0.02) higher than that obtained when the Language section IQ was correlated with total Iowa-Brace. Principal component analysis of the Iowa-Brace Test isolated five factors accounting for 6.7 per cent of the test variance. Only one of these factors showed any relationship to the five factors of the California Test of Mental Maturity. Factor V showed low positive correlations with the Logical Reasoning and Spatial Relations factors. No practically useful relationship seems to exist between the Iowa-Brace Motor Educability Test and the California Test of Mental Maturity. / Education, Faculty of / Curriculum and Pedagogy (EDCP), Department of / Graduate

Two experiments examined the relative ability of different probes in eliciting items occupying various positions in a serial list, the shape of the serial position curve, and the distribution of errors in serial learning. In Experiment I, position, sequential, and backward probes were employed after one presentation of a serial list. The results indicated that all three probes had equal eliciting strengths and that all three probes produced similar serial position curves. Analysis of errors revealed a gradient of generalization around the target word. In Experiment II error distribution and the serial position curve were examined in a learned serial list using the missing scan as a probe. Analysis of correct responding showed flat serial position curves, and the distribution of errors around the target word seemed essentially random. The results were discussed in terms of current theoretical representations of serial learning. / Arts, Faculty of / Psychology, Department of / Graduate

The present study investigated the effects of verbal cues on the learning of visual sequences involving eight elements. Grade two subjects were assigned to three experimental conditions and were given a learning task and a transfer task. The three experimental conditions involved three types of training on a visual sequencing task and differences among the three groups were determined by performance on a posttest after training. The three training conditions were as follows: 1. "Look" group. Subjects were asked to look at the elements in the sequence. 2. "Name" group. Names were attached to the elements of the sequence as they were presented. 3. "Name and Ordinal Position" group. Names and ordinal positions were attached to the elements of the sequence as they were presented. The learning task consisted of a pretest, training according to treatment group, and a posttest. The transfer task consisted of a pretest and a posttest without specific training on transfer task elements. Each subject was put through a four-day cycle: 1. Day 1. Learning task and transfer task pretest. 2. Day 2. Training in visual sequencing according to treatment condition assigned. 3. Day 3. Learning task posttest. 4. Day 4. Transfer task posttest. The findings of this experimental study indicate that attaching names and ordinal positions to the elements in a visual sequence facilitates the learning of the sequence. While the ordinal position factor did not facilitate learning to a statistically significant degree in the learning task, the sample data showed that the group using this cue did perform the task in fewer trials than the "Look" group and the "Name" group. On the transfer task, the performance of the "Name and Ordinal Position" group was significantly better than the other two groups. / Arts, Faculty of / Psychology, Department of / Graduate

36 Ss randomly selected from 76 volunteers from Grade-XII Richmond Secondary School were randomly assigned to six treatments in a 3 x 2 factorial design to test the effect of instructions (time-stressed, accuracy- stressed, or control) and level of motivation (high or low) on performance on three problems of a predetermined, conjunctive, attribute identification task, with stimuli (64 six-dimensional figures) containing the exemplars and non-exemplars of a bi-dimensional concept, and presented simultaneously; and measured in the postulated three phases by: time interval between reception of the task and selection of the first card (Phase 1 - analysis of the problem); index of dimensional change of attributes from the first exemplar (Phase 2 - selection or development of a strategy-plan); and average time per card choice (Phase 3 - execution of a strategy-plan). Two additional measures, number of cards to solution and total time to solution, were observed in order to confirm the successful manipulation of the instructional variable in terms of its behavioral effects. The results suggest that the manipulation of instructional variable was successful. The results indicate that Ss under accuracy-stressed condition took significantly more time during the time interval (Phase 1) and spent significantly more time per card choice (Phase 3), than Ss without instructional treatment (control); and that Ss under time-stressed condition behaved in Phase 1 and 3 in the very same way as Ss without instructional treatment (control). It was observed that Ss under time-stressed condition spent about the same amount of total time to solution as Ss under accuracy-stressed condition, and since Ss under time-stressed condition spent significantly less time per card choice than Ss under accuracy-stressed condition, then these facts indicate that the accuracy-stressed instructions are responsible for the better performance of Ss under accuracy-stressed condition than Ss under time-stressed condition. This suggests that knowledge of the reason for ignoring the time and emphasis on accuracy may induce Ss to take time to analyze the problem and that this opportunity to follow the postulated logical sequence of behavior may improve execution (i.e., performance) on conceptual task. The results failed to confirm third hypothesis that motivation impairs performance under time-stressed condition and improves performance under accuracy- stressed condition. It was observed during the experiment that Ss shifted the focus card from the first exemplar to other positive instances previously identified, and since the focus card used by Ss can not be identified, the index of dimensional change can not be used as an indicator of the strategy-selection behavior in Phase 2. / Arts, Faculty of / Psychology, Department of / Graduate

This study examined the effects of Instruction in transfer of children's Pursuit Rotor motor learning. In addition, the validity of the schema theory was examined. Two learning variables were employed during the learning activities: Instruction with two treatment levels (LTI vs. LIO), and Task Variability with three types of task variations (SPV vs. SV vs. PV). The performance of the learners were measured in terms of the Amount of Time on Target and the Number of Hits on Pursuit Rotor. A 2 (Instruction) by 3 (Task) by 4 (blocks) repeated measure design was adopted for analysing data of the learning activities. Two additional moderator variables of Rest Interval (5 minutes vs. 30 seconds) and Shift of Hands (shifted vs. not-shifted) were included in the design for evaluating transfer effects. The effects of learning and moderator variables were examined. The LTI treatment groups performed the transfer task significantly better than the LIO. There was no significant main effect for the task variability. Only the interaction of Task, Rest, and Shift of Hands was statistically significant. It means that the predicted effect of the task variability is observable only under the limiting condition of 30 seconds period and with hand not shifted (i.e., unilateral transfer). It is concluded that the observed effects of Instruction and Task Variability can be explained by the schema theory. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate

This thesis discusses the valence of stimulus material as a variable in incidental learning.

This case study focused on the ways in which their exposure to violence impacts on primary school children's learning and their cognitive and emotional development in two primary schools in the Cape Flats Township of Mitchell's Plain. The research aimed to explore the extent to which exposure to violence impacts not only on these learners' learning and development but also the various ways in which these children act-out their violent experiences. The fieldwork focused on how the exposure to violence in the household and community influences/affects classroom learning, in particular, learners' oral and written exercises, as well as the quality of their relationships with teachers and other learners. The theoretical framework for this research is based on Vygotsky's Social Developmental theory. This theory assisted me in my investigation and helped me to understand the consequences of exposure to violence for both learning and cognitive and emotional development within a social, or socio-cultural context. I used Bronfenbrenner's ecological models to explain the impact exposure to violence has on human development and cognitive growth within an ecological system. The research approach made use of a qualitative case study of two primary schools, each situated in a different socio-economic area of Mitchell's Plain. The data was collected from participant observations, focus group interviews, and document analysis of learners' writings and drawings. The research found the sample of learners from both schools to have been affected both mentally/cognitively and emotionally by the violence in the area, and that this had caused these learners to lose focus and had certainly affected their academic performance. A key point that emerged was that the sample of learners participating in the study were struggling to create quality relationships with their teachers and peers. The research showed that, causally related to this, children in the study who had been directly or indirectly exposed to violence displayed a lack of trust and confidence in others, and that this often led to discipline issues in the class.

L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées et combinées dans les travaux de la présente thèse pour l’extraction de caractéristiques et la construction de modèles d’ordre réduit. Ainsi, l’application de techniques pilotées par les données pour la détection des caractéristiques d’ensembles de données de combustion turbulente (simulation numérique directe) a été étudiée sur deux flammes H2 / CO: une évolution spatiale (DNS1) et une jet à évolution temporelle (DNS2). Des méthodes telles que l’analyse en composantes principales (ACP), l’analyse en composantes principales locales (LPCA), la factorisation matricielle non négative (NMF) et les autoencodeurs ont été explorées à cette fin. Il a été démontré que divers facteurs pouvaient affecter les performances de ces méthodes, tels que les critères utilisés pour le centrage et la mise à l’échelle des données d’origine ou le choix du nombre de dimensions dans les approximations de rang inférieur. Un ensemble de lignes directrices a été présenté qui peut aider le processus d’identification de caractéristiques physiques significatives à partir de données de flux réactifs turbulents. Des méthodes de compression de données telles que l’analyse en composantes principales (ACP) et les variations ont été combinées à des méthodes d’interpolation telles que le krigeage, pour la construction de modèles ordonnées à prix réduits et calculables pour la prédiction de l’état d’un système de combustion dans des conditions de fonctionnement inconnues ou des combinaisons de modèles valeurs de paramètre d’entrée. La méthodologie a d’abord été testée pour la prévision des flammes 1D avec un nombre croissant de paramètres d’entrée (rapport d’équivalence, composition du carburant et température d’entrée), avec des variantes de l’approche PCA classique, à savoir PCA contrainte et PCA locale, appliquée aux cas de combustion la première fois en combinaison avec une technique d’interpolation. Les résultats positifs de l’étude ont conduit à l’application de la méthodologie proposée aux flammes 2D avec deux paramètres d’entrée, à savoir la composition du combustible et la vitesse d’entrée, qui ont donné des résultats satisfaisants. Des alternatives aux méthodes non supervisées et supervisées choisies ont également été testées sur les mêmes données 2D. L’utilisation de la factorisation matricielle non négative (FNM) pour l’approximation de bas rang a été étudiée en raison de la capacité de la méthode à représenter des données à valeur positive, ce qui permet de ne pas enfreindre des lois physiques importantes telles que la positivité des fractions de masse d’espèces chimiques et comparée à la PCA. Comme méthodes supervisées alternatives, la combinaison de l’expansion du chaos polynomial (PCE) et du Kriging et l’utilisation de réseaux de neurones artificiels (RNA) ont été testées. Les résultats des travaux susmentionnés ont ouvert la voie au développement d’un jumeau numérique d’un four à combustion à partir d’un ensemble de simulations 3D. La combinaison de PCA et de Kriging a également été utilisée dans le contexte de la quantification de l’incertitude (UQ), en particulier dans le cadre de collaboration de données lié (B2B-DC), qui a conduit à l’introduction de la procédure B2B-DC à commande réduite. Comme pour la première fois, le centre de distribution B2B a été développé en termes de variables latentes et non en termes de variables physiques originales. / With the final objective being to developreduced-order models for combustion applications,unsupervised and supervised machine learningtechniques were tested and combined in the workof the present Thesis for feature extraction and theconstruction of reduced-order models. Thus, the applicationof data-driven techniques for the detection offeatures from turbulent combustion data sets (directnumerical simulation) was investigated on two H2/COflames: a spatially-evolving (DNS1) and a temporallyevolvingjet (DNS2). Methods such as Principal ComponentAnalysis (PCA), Local Principal ComponentAnalysis (LPCA), Non-negative Matrix Factorization(NMF) and Autoencoders were explored for this purpose.It was shown that various factors could affectthe performance of these methods, such as the criteriaemployed for the centering and the scaling of theoriginal data or the choice of the number of dimensionsin the low-rank approximations. A set of guidelineswas presented that can aid the process ofidentifying meaningful physical features from turbulentreactive flows data. Data compression methods suchas Principal Component Analysis (PCA) and variationswere combined with interpolation methods suchas Kriging, for the construction of computationally affordablereduced-order models for the prediction ofthe state of a combustion system for unseen operatingconditions or combinations of model input parametervalues. The methodology was first tested forthe prediction of 1D flames with an increasing numberof input parameters (equivalence ratio, fuel compositionand inlet temperature), with variations of the classicPCA approach, namely constrained PCA and localPCA, being applied to combustion cases for the firsttime in combination with an interpolation technique.The positive outcome of the study led to the applicationof the proposed methodology to 2D flames withtwo input parameters, namely fuel composition andinlet velocity, which produced satisfactory results. Alternativesto the chosen unsupervised and supervisedmethods were also tested on the same 2D data.The use of non-negative matrix factorization (NMF) forlow-rank approximation was investigated because ofthe ability of the method to represent positive-valueddata, which helps the non-violation of important physicallaws such as positivity of chemical species massfractions, and compared to PCA. As alternative supervisedmethods, the combination of polynomial chaosexpansion (PCE) and Kriging and the use of artificialneural networks (ANNs) were tested. Results from thementioned work paved the way for the developmentof a digital twin of a combustion furnace from a setof 3D simulations. The combination of PCA and Krigingwas also employed in the context of uncertaintyquantification (UQ), specifically in the bound-to-bounddata collaboration framework (B2B-DC), which led tothe introduction of the reduced-order B2B-DC procedureas for the first time the B2B-DC was developedin terms of latent variables and not in terms of originalphysical variables.

Assessments improve student learning. More than 50 years ago, Benjamin Bloom showed how to conduct this process in practical and highly effective ways when he described the practice of mastery learning (Bloom, 1968, 1971). Open-ended problems in assignments, as opposed to more closed-ended problems where there are a small set of known correct responses, offer an opportunity for students to demonstrate their understanding by articulating their underlying thought processes. In such problems, students are required to explain in a sentence or two, how to solve a particular problem or how they arrived at a solution. Open-ended responses stimulate a thought process in a student and allow teachers to better evaluate the student’s deeper understanding of a topic beyond what can be observed in other problem types. Due to the open-ended nature of student responses to these problems, however, it is sometimes difficult for teachers to devote time to assessing student work, which causes students to apply lower effort or disengage from such problems if it is believed that a teacher is unlikely to attend to it. In order to promote better student engagement with these open-ended questions and to motivate them to apply more effort in answering these questions, I have built an infrastructure to conduct RCTs(Randomized Control Trials) with open-ended problems within ASSISTments, an online assessment tool; I have built an infrastructure that caters to machine learning models for the automated assessment of the student work. I am using this infrastructure to design an RCT that will evaluate the effect of prompted self-revision on the quality of the student responses.

No description available.