Self-regulated learning in higher education : strategies adopted by computer programming students

Trabalho apresentado em PAEE/ALE’2016, 8th International Symposium on Project Approaches in Engineering Education (PAEE) and 14th Active Learning in Engineering Education Workshop (ALE)

From the workplace to the classroom:examining the impact of self-leadership learning strategies on higher educational attainment and success

Self-leadership is a concept from the organisational and management literature broadly combining processes of self-goal setting, self-regulation and self-motivation. Research has typically focused on the impact of self-leadership on work performance outcomes, with little attention to potential benefits for learning and development. In this paper, we employ a longitudinal design to examine the association of a number of processes of self-leadership with higher educational attainment in a sample of business students (N = 150). Self-reported use of strategies related to behavioural, cognitive and motivational aspects of self-leadership were measured in the first semester of the academic year, and correlated with end-of year grade point average. We found that in particular, self-goal setting, pro-active goal-related behaviour, behaviour regulation and direction, motivational awareness, and optimism were all significant predictors of educational attainment. We discuss implications for educational research and for teachers and tutors in practice.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Collaborative technologies, higher order thinking and self-sufficient learning: A case study of adult learners

The inclusion of online elements in learning environments is becoming commonplace in Post Compulsory Education. A variety of research into the value of such elements is available, and this study aims to add further evidence by looking specifically at the use of collaborative technologies such as online discussion forums and wikis to encourage higher order thinking and self-sufficient learning. In particular, the research examines existing pedagogical models including Salmon’s five-stage model, along with other relevant literature. A case study of adult learners in community-based learning centres forms the basis of the research, and as a result of the findings, an arrow model is suggested as a framework for online collaboration that emphasises the learner, mentions pre-course preparation and then includes three main phases of activity: post, interact and critique. This builds on Salmon’s five-stage model and has the benefit of being flexible and responsive, as well as allowing for further development beyond the model, particularly in a blended learning environment.

Fostering postgraduate student engagement: online resources supporting self-directed learning in a diverse cohort

The research question for this study was: ‘Can the provision of online resources help to engage and motivate students to become self-directed learners?’ This study presents the results of an action research project to answer this question for a postgraduate module at a research-intensive university in the United Kingdom. The analysis of results from the study was conducted dividing the students according to their programme degree – Masters or PhD – and according to their language skills. The study indicated that the online resources embedded in the module were consistently used, and that the measures put in place to support self-directed learning (SDL) were both perceived and valued by the students, irrespective of their programme or native language. Nevertheless, a difference was observed in how students viewed SDL: doctoral students seemed to prefer the approach and were more receptive to it than students pursuing their Masters degree. Some students reported that the SDL activity helped them to achieve more independence than did traditional approaches to teaching. Students who engaged with the online resources were rewarded with higher marks and claimed that they were all the more motivated within the module. Despite the different learning experiences of the diverse cohort, the study found that the blended nature of the course and its resources in support of SDL created a learning environment which positively affected student learning.

Self-supervised Information Retrieval basato su deep metric learning

La tesi ha lo scopo di ricercare, esaminare ed implementare un sistema di Machine Learning, un Recommendation Systems per precisione, che permetta la racommandazione di documenti di natura giuridica, i quali sono già stati analizzati e categorizzati appropriatamente, in maniera ottimale, il cui scopo sarebbe quello di accompagnare un sistema già implementato di Information Retrieval, istanziato sopra una web application, che permette di ricercare i documenti giuridici appena menzionati.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Number of options in a movement sequence affects learners' behavior in a self-controlled practice condition

Self controlling practice implies a process of decision making which suggests that the options in a self controlled practice condition could affect learners The number of task components with no fixed position in a movement sequence may affect the (Nay learners self control their practice A 200 cm coincident timing track with 90 light emitting diodes (LEDs)-the first and the last LEDs being the warning and the target lights respectively was set so that the apparent speed of the light along the track was 1 33 m/sec Participants were required to touch six sensors sequentially the last one coincidently with the lighting of the tar get light (timing task) Group 1 (n=55) had only one constraint and were instructed to touch the sensors in any order except for the last sensor which had to be the one positioned close to the target light Group 2 (n=53) had three constraints the first two and the last sensor to be touched Both groups practiced the task until timing error was less than 30 msec on three consecutive trials There were no statistically significant differences between groups in the number of trials needed to reach the performance criterion but (a) participants in Group 2 created fewer sequences corn pared to Group 1 and (b) were more likely to use the same sequence throughout the learning process The number of options for a movement sequence affected the way learners self-controlled their practice but had no effect on the amount of practice to reach criterion performance.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Stochastic stability analysis for the constant-modulus algorithm

We derive an easy-to-compute approximate bound for the range of step-sizes for which the constant-modulus algorithm (CMA) will remain stable if initialized close to a minimum of the CM cost function. Our model highlights the influence, of the signal constellation used in the transmission system: for smaller variation in the modulus of the transmitted symbols, the algorithm will be more robust, and the steady-state misadjustment will be smaller. The theoretical results are validated through several simulations, for long and short filters and channels.

Use of the web to provide learning support for a large metabolism and nutrition class

To support student learning in a large Metabolism and Nutrition class, we have introduced a web-based package, using a commercially available program, WebCT. The package was developed at a minimal cost and with limited resources. In addition to downloadable (PDF) versions of lecture Powerpoint presentations, tutorial outlines and a practical class exercise, web-based self-directed learning exercises were included to reinforce and extend lecture material in an active learning environment. The web-site also contained a variety of formative and summative assessment tasks that examined both factual recall and higher order thinking Detailed course information, timetables and a bulletin board were also readily accessible. Student usage of the site was generally high, but varied widely between individual students. Students who achieved a high overall score for the course completed on average three times as many formative assessment items and achieved a higher score for all tests than students who did poorly. Student feedback about the site was very positive with the majority of students reporting that the course material and assessment items that were available were useful to their learning. Administration of the course was also facilitated. (C) 2001 IUBMB. Published by Elsevier Science Ltd. All rights reserved.

Assessing self- and peer-assessment: The students' views

Self- and peer-assessment are being used increasingly in higher education, to help assign grades to students' work and to help students to learn more effectively. However, in spite of this trend there is little in the published literature on how students view these methods. In this paper we present an analysis of the views of a large number of students (N = 233) who had just experienced self- and peer-feedback as part of one of their subjects. It is a rarely questioned commonplace in the literature that in order to gain benefit from peer and self-assessment schemes students first need training in the specific scheme being used; ideally they will play a role in devising the scheme. The intervention reported here, which involved a large (N = 233) group of students, included no such measures. The results show that students felt, nonetheless, that they benefited from the intervention. The results also present prima facie evidence that training or other measures to further involve the students in the peer and self-assessment scheme might be beneficial. Our analysis of students' views revealed eight general dimensions under which are grouped twenty higher order themes. The results both support and extend previous research and give a more detailed picture than previously available. The general dimensions found were: Difficult; Gained Better Understanding of Marking; Discomfort; Productive (including learning benefits and improved work); Problems with Implementation; Read Others' Work; Develop Empathy (with assessing staff); and, Motivation (especially motivation to impress peers). The practical implications of these findings are discussed.

The Building Partnerships Program: An Approach to Community-based Learning for Medical Students in Australia

The Building Partnerships Program at the University of Queensland, Australia seeks to address the dual challenge of preparing doctors who are responsive to the community while providing a meaningful context for social sciences learning. Through partnerships with a diverse range of community agencies, the program offers students opportunities to gain non-clinical perspectives on health and illness through structured learning activities including: family visits; community agency visits and attachments; and interview training. Students learn first-hand about psychosocial influences on health and how people manage health problems on a day-to-day basis. They also gain insights into the work of community agencies and how they as future doctors might work in partnership with them to enhance patient care. We outline the main components of the program, identify challenges and successes from student and community agency perspectives, and consider areas that invite further development.