Approaches to Learning

Looks at some of the models of learning and discusses how they apply to university students

Evoked prior learning experience and approach to learning as predictors of academic achievement

Background. In separate studies and research from different perspectives, five factors are found to be among those related to higher quality outcomes of student learning (academic achievement). Those factors are higher self-efficacy, deeper approaches to learning, higher quality teaching, students’ perceptions that their workload is appropriate, and greater learning motivation. University learning improvement strategies have been built on these research results. Aim. To investigate how students’ evoked prior experience, perceptions of their learning environment, and their approaches to learning collectively contribute to academic achievement. This is the first study to investigate motivation and self-efficacy in the same educational context as conceptions of learning, approaches to learning and perceptions of the learning environment. Sample. Undergraduate students (773) from the full range of disciplines were part of a group of over 2,300 students who volunteered to complete a survey of their learning experience. On completing their degrees 6 and 18 months later, their academic achievement was matched with their learning experience survey data. Method. A 77-item questionnaire was used to gather students’ self-report of their evoked prior experience (self-efficacy, learning motivation, and conceptions of learning), perceptions of learning context (teaching quality and appropriate workload), and approaches to learning (deep and surface). Academic achievement was measured using the English honours degree classification system. Analyses were conducted using correlational and multi-variable (structural equation modelling) methods. Results. The results from the correlation methods confirmed those found in numerous earlier studies. The results from the multi-variable analyses indicated that surface approach to learning was the strongest predictor of academic achievement, with self-efficacy and motivation also found to be directly related. In contrast to the correlation results, a deep approach to learning was not related to academic achievement, and teaching quality and conceptions of learning were only indirectly related to achievement. Conclusions. Research aimed at understanding how students experience their learning environment and how that experience relates to the quality of their learning needs to be conducted using a wider range of variables and more sophisticated analytical methods. In this study of one context, some of the relations found in earlier bivariate studies, and on which learning intervention strategies have been built, are not confirmed when more holistic teaching–learning contexts are analysed using multi-variable methods.

Videogames, learning, and the social context : approaches to an ethnographic case study

Focusing on the role-playing simulation game SCAPE (Sustainability, Community and Planning Education), this paper proposes that potential disparities between game design practice and the meaning-making process of the players need to be addressed in a wider ecology of learning. The cultural setting of the gameplay experience, and also the different levels of engagement of the players, can be seen to pose vital questions, which are in and of themselves objects of inquiry. This paper argues that ethnographic participant-observation, which is a recognized approach in game studies, allows taking the wider ecology of learning into account to explore the various relations that shape the gameplay.

Machine learning approaches to analysing textual injury surveillance data: A systematic review

Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.

Teaching in Higher Education : From Content-focused to Learning-focused Approaches to Teaching

The aim of this dissertation was to explore teaching in higher education from the teachers’ perspective. Two of the four studies analysed the effect of pedagogical training on approaches to teaching and on self-efficacy beliefs of teachers on teaching. Of these two studies, Study I analysed the effect of pedagogical training by applying a cross-sectional setting. The results showed that short training made teachers less student-centred and decreased their self-efficacy beliefs, as reported by the teachers themselves. However, more constant training enhanced the adoption of a student-centred approach to teaching and increased the self-efficacy beliefs of teachers as well. The teacher-focused approach to teaching was more resistant to change. Study II, on the other hand, applied a longitudinal setting. The results implied that among teachers who had not acquired more pedagogical training after Study II there were no changes in the student-focused approach scale between the measurements. However, teachers who had participated in further pedagogical training scored significantly higher on the scale measuring the student-focused approach to teaching. There were positive changes in the self-efficacy beliefs of teachers among teachers who had not participated in further training as well as among those who had. However, the analysis revealed that those teachers had the least teaching experience. Again, the teacher-focused approach was more resistant to change. Study III analysed approaches to teaching qualitatively by using a large and multidisciplinary sample in order to capture the variation in descriptions of teaching. Two broad categories of description were found: the learning-focused and the content-focused approach to teaching. The results implied that the purpose of teaching separates the two categories. In addition, the study aimed to identify different aspects of teaching in the higher-education context. Ten aspects of teaching were identified. While Study III explored teaching on a general level, Study IV analysed teaching on an individual level. The aim was to explore consonance and dissonance in the kinds of combinations of approaches to teaching university teachers adopt. The results showed that some teachers were clearly and systematically either learning- or content-focused. On the other hand, profiles of some teachers consisted of combinations of learning- and content-focused approaches or conceptions making their profiles dissonant. Three types of dissonance were identified. The four studies indicated that pedagogical training organised for university teachers is needed in order to enhance the development of their teaching. The results implied that the shift from content-focused or dissonant profiles towards consonant learning-focused profiles is a slow process and that teachers’ conceptions of teaching have to be addressed first in order to promote learning-focused teaching.

The CAMEL Project: Collaborative Approaches to the Management of E-Learning

CAMEL is short for Collaborative Approaches to the Management of e-Learning and was a project funded by the HEFCE Leadership, Governance and Management programme. It set out to explore how institutions who were making effective use of e-learning and who were collaborating in regional lifelong learning partnerships might be able to learn from each other in a Community of Practice based around study visits to each of the partner institutions. This short publication highlights some of the things CAMEL participants found out about e-learning and about each other.

Curriculum innovation: pragmatic approaches to transforming learning and teaching through technologies

This report draws together outcomes from the JISC Curriculum Delivery Programme on behalf of JISC and includes recommendations for further investigation.

Collaborative approaches to the management of e-learning (CAMEL)

A teaching and learning project funded by the Office of Learning, Enhancement, Access and Participation (LEAP) at the University of Greenwich.

Bayesian and L1 Approaches to Sparse Unsupervised Learning

The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularisation often dramatically underperforms in terms of predictive performance when compared with other methods for inferring sparsity. We focus on unsupervised latent variable models, and develop L1 minimising factor models, Bayesian variants of "L1", and Bayesian models with a stronger L0-like sparsity induced through spike-and-slab distributions. These spike-and-slab Bayesian factor models encourage sparsity while accounting for uncertainty in a principled manner and avoiding unnecessary shrinkage of non-zero values. We demonstrate on a number of data sets that in practice spike-and-slab Bayesian methods outperform L1 minimisation, even on a computational budget. We thus highlight the need to re-assess the wide use of L1 methods in sparsity-reliant applications, particularly when we care about generalising to previously unseen data, and provide an alternative that, over many varying conditions, provides improved generalisation performance.