This thesis explored relationships (1) between composite and constituent processes of self-regulated learning (SRL) and three sport performance groups, (2) between SRL and different practice variables, and (3) whether these associations depended on an athlete’s consideration of future consequences (CFC). Athletes (N = 272; 200 males; 18-35 yrs; M practice = 13.55 hours/week) completed survey measures for SRL, weekly training including deliberate practice (DP), performance level and CFC. Higher scores in composite SRL were associated with a greater chance of belonging to an elite group, compared to a less-elite and a recreationally competitive group. Self-monitoring predicted greater likelihood of membership in less-elite and elite groups compared to the recreationally competitive group. Self-monitoring predicted greater engagement in total DP hours, and DP in supervised and unsupervised settings. Effort, self-efficacy, and planning were notable in some results, but contributions were less significant. CFC had no moderating effect, however it was correlated with SRL.

No description available.

Le lien qui existe entre la diversité et la productivité des écosystèmes constitue un sujet central en écologie. De nombreuses études ont montré une relation positive entre la diversité et la productivité des forêts, ainsi que leur résistance à différents stress comme à la sécheresse. En revanche, peu de travaux ont permis de comprendre les mécanismes à l'origine des avantages observés dans les forêts à plusieurs espèces comparés aux forêts monospécifiques. En particulier, en raison de la difficulté d'accès aux racines, le rôle du compartiment souterrain dans les interactions entre espèces est particulièrement méconnu. L'objectif de ma thèse a été d'étudier l’influence des interactions entre espèces d’arbres sur le système racinaire du hêtre (Fagus sylvatica) sous différents niveaux de contrainte hydrique. Ces travaux ont montré que, au stade jeune plant, la compétition souterraine entre hêtre, chêne et pin était forte et que mélanger les espèces à ce stade pouvait influencer la croissance du hêtre. La disponibilité en eau a un fort impact sur la croissance des plantules de hêtre mais la présence d’autres espèces à proximité des jeunes plants de hêtre n’a pas amélioré leur réponse à la sécheresse. Au stade adulte, dans une plantation forestière, nous avons montré que la présence de l’érable n’influençait que légèrement la profondeur d’extraction de l’eau du hêtre ou la distribution verticale de ses racines. Nous concluons (i) à une absence de séparation des niches souterraines entre ces deux espèces, tant au niveau spatial que fonctionnel (vis à vis de l'acquisition de l’eau), et (ii) que les mécanismes d’interaction souterraine ne semblent pas expliquer l’origine de la productivité plus forte dans la zone de mélange que dans les zones de monocultures. Enfin, j'ai montré que les peuplements mélangés hébergent une communauté fongique plus riche par rapport aux peuplements purs. Cette plus forte richesse de la communauté fongique dans le mélange pourrait contribuer à une meilleure acquisition des ressources hydriques et minérales dans le mélange. Mon travail suggère que les interactions souterraines entre le hêtre et d’autres espèces forestières ne sont probablement pas le mécanisme principal expliquant les effets positifs des mélanges sur la productivité / The link between species diversity and ecosystem productivity is a central issue in ecology. Numerous studies have shown a positive relationship between forest diversity and productivity, as well as a greater resistance to various stresses such as droughts. On the other hand, few studies demonstrated the mechanisms behind the benefits observed in multi-species forests compared to monospecific ones. In particular, the role of belowground interactions among species in explaining the origin of positive effects of species diversity on ecosystem functioning is unknown. The aim of my PhD work was to study the influence of tree species interactions on the root development of beech (Fagus sylvatica) under different levels of water conditions. We showed that at the seedling stage, underground competition between beech, oak and pine was strong and that mixing these species could influence the growth of the beech. Water availability had a strong impact on the growth of beech seedlings, but the presence of seedlings of other species competing with beech did not particularly improve its response to drought. At the adult stage, in a forest plantation, we showed that maple trees competing with beech did not strongly modify the mean depth of soil water extraction of beech trees or the vertical distribution of beech roots. We conclude that (i) there was no separation of the underground ecological niches of these two species, both spatially and functionally (with respect to water acquisition), and (ii) that the mechanisms of belowground interaction between these two species do not seem to explain the origin of the higher productivity in the mixture than in the monoculture zone. Finally, the richest fungal communities were found in the mixed species zone: we hypothesize that greater fungal community richness in the mixture may improve water and nutrient acquisition and then contribute to higher productivity in the mixed species zone. My work suggests that underground interactions between beech and other forest species are probably not the main mechanism explaining beneficial effects of mixtures on productivity

Most work that is done within machine learning today uses statistical methods which assume that the data is identically and independently distributed. However, the problem domains that we face in the real world are often much more complicated and present both complex relational/logical parts as well as parts with uncertainty. Statistical relational learning (SRL) is a sub-field of machine learning and A.I. that tries to solve these limitations by combining both relational and statistical learning and has become a big research sector in recent years. This thesis will present SRL further and specifically introduce, test and review one of the implementations, namely Bayesian logic programs. / Idag används inom maskininlärning nästan alltid statistiska metoder som antar att datat för lärande är identiskt och oberoende distribuerat. Men de problemområden som vi står inför i den verkliga världen är ofta mycket mer komplicerade och har både komplexa relationella/logiska delar samt osäkerhet. Statistiskt relationslärande (SRL) är en del av maskininlärning och A.I. som försöker lösa dessa begränsningar genom att kombinera både relationer och statistiskt lärande och har på senare år blivit ett stort forskningsområde. Denna avhandling presenterar SRL mer i detalj och utreder och testar en specifik implementation, Bayesianska logikprogram.

Organisations are increasingly using e-learning as a tool to train employees due to its flexibility and cost-efficiency. Training and development plays a central part in onboarding of new employees where e-learning is used to help new employees getting up to speed in their roles and become an effective member in the organisation. Self-regulatory learning is an important theory to consider when managing learning in organisations. It provides insight on what learners do in educational environments in order to learn, and what important underlying behaviours that make a learning process flourish. Researchers call for an examination of the learning process of e-learners. Understanding learning processes can deepen our understanding of what support employees need and thereby provide important insights in how to support learners during e-learning. This paper presents a qualitative study, which applies four elements of self-regulatory learning to the phenomenon of e-learning during onboarding in IT-organisations. The aim is to understand new employees e-learning processes in their new roles. Our findings display the prevalent role of goal setting and help seeking during onboarding. This research identifies different types of goals and how a combination of distant and proximal goals provide learners with a good foundation for professional development during onboarding. Furthermore, we acknowledge the importance of help seeking within the organisational context and its vital role in early stages of using e-learning systems.

Several real world tasks involve data that is uncertain and relational in nature. Traditional approaches like first-order logic and probabilistic models either deal with structured data or uncertainty, but not both. To address these limitations, statistical relational learning (SRL), a new area in machine learning integrating both first-order logic and probabilistic graphical models, has emerged in the recent past. The advantage of SRL models is that they can handle both uncertainty and structured/relational data. As a result, they are widely used in domains like social network analysis, biological data analysis, and natural language processing. Bayesian Logic Programs (BLPs), which integrate both first-order logic and Bayesian net- works are a powerful SRL formalism developed in the recent past. In this dissertation, we develop approaches using BLPs to solve two real world tasks – plan recognition and machine reading. Plan recognition is the task of predicting an agent’s top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. In the first part of the dissertation, we develop an approach to abductive plan recognition using BLPs. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for abductive plan recognition as is. Therefore, we extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). In the second part of the dissertation, we apply BLPs to the task of machine reading, which involves automatic extraction of knowledge from natural language text. Most information extraction (IE) systems identify facts that are explicitly stated in text. However, much of the information conveyed in text must be inferred from what is explicitly stated since easily inferable facts are rarely mentioned. Human readers naturally use common sense knowledge and “read between the lines” to infer such implicit information from the explicitly stated facts. Since IE systems do not have access to common sense knowledge, they cannot perform deeper reasoning to infer implicitly stated facts. Here, we first develop an approach using BLPs to infer implicitly stated facts from natural language text. It involves learning uncertain common sense knowledge in the form of probabilistic first-order rules by mining a large corpus of automatically extracted facts using an existing rule learner. These rules are then used to derive additional facts from extracted information using BLP inference. We then develop an online rule learner that handles the concise, incomplete nature of natural-language text and learns first-order rules from noisy IE extractions. Finally, we develop a novel approach to calculate the weights of the rules using a curated lexical ontology like WordNet. Both tasks described above involve inference and learning from partially observed or incomplete data. In plan recognition, the underlying cause or the top-level plan that resulted in the observed actions is not known or observed. Further, only a subset of the executed actions can be observed by the plan recognition system resulting in partially observed data. Similarly, in machine reading, since some information is implicitly stated, they are rarely observed in the data. In this dissertation, we demonstrate the efficacy of BLPs for inference and learning from incomplete data. Experimental comparison on various benchmark data sets on both tasks demonstrate the superior performance of BLPs over state-of-the-art methods. / text

The purpose of this study was to explore the relationship between active procrastination, self-regulated learning and academic achievement. Participants included 108 undergraduate students enrolled in a first-year elective course at a Canadian university. Students reported their level of active procrastination, cognitive and metacognitive strategy use, self-efficacy for learning and performance, goal quality and self-reported goal attainment over the semester. Measures included the self-report Active Procrastination Scale (APS; Choi & Moran, 2009), the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich Smith, Garcia, & McKeachie, 1991) and weekly reflections. Findings revealed: (a) active procrastination was significantly positively related to academic achievement, (b) the ability to meet deadlines was the component of active procrastination most related to SRL variables, and (c) self-reported goal attainment accounted for the most variance in ability to meet deadlines score. Further research is needed to explore the central role of ability to meet deadlines in active procrastination and the order in which SRL variables, active procrastination and negative influence of procrastination predict academic achievement. / Graduate

For the vast majority of languages, Natural Language Processing (NLP) tools are either absent entirely, or leave much to be desired in their final performance. Despite having nearly 4 million speakers, one such low-resource language is Galician. In an effort to expand available NLP resources, this project sought to construct a dataset for Semantic Role Labeling (SRL) and produce a baseline for future research to use in comparisons. SRL is a task which has shown success in amplifying the final output for various NLP systems, including Machine Translation and other interactive language models. This project was successful in that fact and produced 24 SRL models and two SRL datasets; one Galician and one Spanish. mBERT and XLM-R were chosen as the baseline architectures; additional models were first pre-trained on the SRL task in a language other than the target to measure the effects of transfer-learning. Scores are reported on a scale of 0.0-1.0. The best performing Galician SRL model achieved an f1 score of 0.74, introducing a baseline for future Galician SRL systems. The best performing Spanish SRL model achieved an f1 score of 0.83, outperforming the baseline set by the 2009 CoNLL Shared Task by 0.025. A pre-processing method, verbal indexing, was also introduced which allowed for increased performance in the SRL parsing of highly complex sentences; effects were amplified in scenarios where the model was both pre-trained and fine-tuned on datasets utilizing the method, but still visible even when only used during fine-tuning. / För de allra flesta språken saknas språkteknologiska verktyg (NLP) helt, eller för dem de var i finns tillgängliga är dessa verktygs prestanda minst sagt, sämre än medelmåttig. Trots sina nästan 4 miljoner talare, är galiciska ett språk med brist på tillräckliga resurser. I ett försök att utöka tillgängliga NLP-resurser för språket, konstruerades i detta projekt en uppsättning data för så kallat Semantic Role Labeling (SRL) som sedan användes för att utveckla grundläggande SRL-modeller att falla tillbaka på och jämföra  med i framtida forskning. SRL är en uppgift som har visat framgång när det gäller att förstärka slutresultatet för olika NLP-system, inklusive maskinöversättning och andra interaktiva språkmodeller. I detta avseende visade detta projekt på framgång och som del av det utvecklades 24 SRL-modeller och två SRL-datauppsåttningar; en galicisk och en spansk. mBERT och XLM-R valdes som baslinjearkitekturer; ytterligare modeller tränades först på en SRL-uppgift på ett språk annat än målspråket för att mäta effekterna av överföringsinlärning (Transfer Learning) Poäng redovisas på en skala från 0.0-1.0. Den galiciska SRL-modellen med bäst prestanda uppnådde ett f1-poäng på 0.74, vilket introducerar en baslinje för framtida galiciska SRL-system. Den bästa spanska SRL-modellen uppnådde ett f1-poäng på 0.83, vilket överträffade baslinjen +0.025 som sattes under CoNLL Shared Task 2009. I detta projekt introduceras även en ny metod för behandling av lingvistisk data, så kallad verbalindexering, som ökade prestandan av mycket komplexa meningar. Denna prestandaökning först märktes ytterligare i de scenarier och är en modell både förtränats och finjusterats på uppsättningar data som behandlats med metoden, men visade även på märkbara förbättringar då en modell endast genomgått finjustering. / Para la gran mayoría de los idiomas, las herramientas de procesamiento del lenguaje natural (NLP) están completamente ausentes o dejan mucho que desear en su desempeño final. A pesar de tener casi 4 millones de hablantes, el gallego continúa siendo un idioma de bajos recursos. En un esfuerzo por expandir los recursos de NLP disponibles, el objetivo de este proyecto fue construir un conjunto de datos para el Etiquetado de Roles Semánticos (SRL) y producir una referencia para que futuras investigaciones puedan utilizar en sus comparaciones. SRL es una tarea que ha tenido éxito en la amplificación del resultado final de varios sistemas NLP, incluida la traducción automática, y otros modelos de lenguaje interactivo. Este proyecto fue exitoso en ese hecho y produjo 24 modelos SRL y dos conjuntos de datos SRL; uno en gallego y otro en español. Se eligieron mBERT y XLM-R como las arquitecturas de referencia; previamente se entrenaron modelos adicionales en la tarea SRL en un idioma distinto al idioma de destino para medir los efectos del aprendizaje por transferencia. Las puntuaciones se informan en una escala de 0.0 a 1.0. El modelo SRL gallego con mejor rendimiento logró una puntuación de f1 de 0.74, introduciendo un objetivo de referencia para los futuros sistemas SRL gallegos. El modelo español de SRL con mejor rendimiento logró una puntuación de f1 de 0.83, superando la línea base establecida por la Tarea Compartida CoNLL de 2009 en 0.025. También se introdujo un método de preprocesamiento, indexación verbal, que permitió un mayor rendimiento en el análisis SRL de oraciones muy complejas; los efectos se amplificaron cuando el modelo primero se entrenó y luego se ajustó con los conjuntos de datos que utilizaban el método, pero los efectos aún fueron visibles incluso cuando se lo utilizó solo durante el ajuste.

No description available.

The use of instructional technology has opened up new avenues in education with broad implications in the foreign or additional language (L2) learning context. One of the research priorities is to explore student perceptions of the use of such modern means in their education which otherwise might not be anticipated. The present study aimed to determine (a) the perceived affordances as well as limitations of the information and communication technology (ICT) pedagogical application in coded corrective feedback (e-feedback) provision on L2 writing, (b) English as a foreign language (EFL) learners’ perspectives on using e-feedback to reduce their local and global mistakes, and (c) the type of self-regulated learning (SRL) behaviours, according to EFL students’ self-reports, electronic feedback and learning logs called forth in cognitive, affective, and metacognitive domains. The participants (n=48) were high-intermediate to advanced EFL learners from four cohorts enrolled on an International English Language Testing System (IELTS) preparation course in a branch of the Institute of Science and Technology in Tehran. Each cohort went through 84 face-to-face tutorial sessions in four months. During this period, they also wrote essays and received e-feedback on 12 IELTS Writing Task 2 prompts with a minimum of drafting work three times for each on an e-learning platform (www.ekbatani.ir) specially designed for this study. The data from all four cohorts were collected over the course of 11 months, using semi-structured interviews, online structured and unstructured learning logs, and an open-ended questionnaire to provide an in-depth picture of student perceptions of this technology mediation. Through a purely qualitative research design, the log, interview, and open-ended questionnaire data were analysed, categorised and coded. The findings represented students’ perceptions of the benefits of the e-feedback and learning logs as (i) offering a motivating and empowering means of providing EFL writing support, (ii) enhancing the thinking and problem-solving processes, (iii) a flexible and fast scaffolding approach for L2 writing improvement, and (iv) encouraging student writers’ active knowledge construction by helping them notice mistakes, focus on writing specifics, overcome the fear of writing, and grow confidence in L2 learning. The self-reported data indicated perceived limitations including (i) the time-consuming nature of the e-feedback processes, (ii) the occasional need for face-to-face discussions, peer feedback addition, providing supplements to e-feedback such as on-demand e-tutorials, and (iii) increased workload for the teacher in proportion to the number of students. Specific writing improvement was perceived to be locally in the use of punctuation signs and grammar, in spelling skills and the scope of vocabulary; and globally in organising ideas, finding ideas in the form of blueprints, and developing ideas into full-length essays. The student perceptions demonstrated that the learner-centred e-feedback environment created different affordances for students’ cognitive, affective, and metacognitive behaviours: (i) cognitively, it assisted the use and development of various learning strategies, enhanced student EFL writing experience, and increased awareness of error patterns in their essays; (ii) affectively, it supported students’ motivational processes, ability to appraise their progress, restore, and sustain positivity, and greater perceived self-efficacy beliefs in their own L2 writing skills; finally, (iii) metacognitive affordances included the ability to rethink and amend their plans as well as seek out support, ability to reflect on the writing processes holistically, ability to self-monitor to remain on course, and ability to devise and implement a plan of action mostly by finding a strategy to deal with mistakes and by taking greater caution in writing their future drafts. Despite arising from a particular contextual framework with the experience of particular cohorts of students, the findings can hopefully be of value to researchers and practitioners in the fields of online language pedagogy, second language acquisition (SLA), EFL writing, and computer-assisted language learning (CALL) with communication uses. The findings can assist language courseware designers, e-feedback platform developers, and L2 writing course administrators to support and enhance their practices and decisions, especially in providing and implementing ICT and SRL initiatives in EFL writing.