Automatically learning structural units in educational videos with the hierarchical hidden Markov models

In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations.

Investigating machine learning techniques for detection of depression using structural MRI volumetric features

Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.

Structural equation modeling for evaluating the user perceptions of E-learning effectiveness in higher education

A crucial prerequisite for sustainable e-learning is the understanding of learners’ preferences for various pedagogical strategies, technologies, and the management of learning resources. This paper presents an empirical study aiming to empirically test the theoretical (pedagogies, technologies and management) (PTM) model on the preference of learners and on the perceived impact of the effectiveness of e-learning. This study uses structural equation modelling (SEM) to identify the critical dimensions in the PTM model for augmenting the effectiveness of e-learning. This leads to the development of a PTM model with the path coefficients showing weak to strong relationships ranging from 0.15 to 0.42 with acceptable significance levels. The results support the hypothesis that management, technology, resources and metadata ontology dimensions affect the effectiveness of elearning both directly and indirectly through enhancing the management effectiveness of learning resources. However, the result does not support positive influence of pedagogical strategy per se on e-learning effectiveness. The implications of this study indicate the criticality of effective management of learning resources to enhance e-learning effectiveness.

Understanding the learning approaches of first-year accounting students

This study evolves from the broader educational research that indicates the characteristics of the student and the perceptions of the teaching/learning environment influence the quality of student learning. The model of learning developed in the paper is based on Biggs' (1987a) model of student learning together with the congruence model of vocational interests and work environments proposed by Holland (1985,1992). The model of learning was tested using a sample of 826 first year accounting students using structural equation modelling (SEM).

The findings provide substantive information about the learning approaches of students with vocational interests congruent with the task demands of a first year accounting course. Additionally, there is strong support for the association between student perceptions of the teaching/learning environment and approaches to learning. In particular the re specified model of student learning identifies the relationship between student perceptions of the appropriateness of workload and the adoption of a surface approach to learning.

(W)reading, wrangling and the rhythm of the text: enhancing the education of young boys with game-based learning

W)reading, wrangling and the rhythm of the text: enhancing the education of young boys with game-based learning Connecting curriculum content to young people's engagement with texts outside of the classroom is increasingly recognised as a method of providing challenging learning environments (Beavis 1999). The introduction of games-based learning in areas such as literacy provides young people with a structural and conceptual framework with which many, particularly young males, may be familiar...

Learning through research : a regional university and its community

Regional universities bring a research capacity to their home locations that is rarely available through other mechanisms in the region. University initiated research projects conducted locally can provide an opportunity for regional communities to examine their practices through a different lens. Through these projects, researchers in regional universities whose research includes sites internal and external to the region are able to connect their region to national and global contexts. Research presents many opportunities for regional universities and their communities to learn together.

There is some evidence that policy-makers are aware of the importance of behavioural relationships in the engagement of regional universities with communities. Policy documents tend to focus on the macro, institutional level benefits, structural incentives and impediments to university and community engagement. This paper examines research from one faculty based on a regional university campus: the Faculty of Education at the University of Tasmania in Launceston in Australia. It takes a micro view, considering benefits and factors influencing success for small research teams and individual researchers and their community research associates. A learning community approach, where synergies from collaboration can generate new knowledge for the benefit of all university and community players, emerges as an effective model for regional engagement through research.

I haven't changed bigots but … : reflections on the impact of teacher professional learning in sexuality education

This paper reports on aspects of an Australian study into the factors and conditions that make it possible for secondary school health education teachers to include and affirm gender and sexual diversity in their teaching. The study examined the impact of a two-day intervention designed to prepare teachers to use a major new government-funded teaching and learning resource called Talking Sexual Health. The study found that whilst there was a range of personal and structural barriers inhibiting change, professional development and access to teaching and learning resources could indeed impact positively on teachers' willingness and ability to include and affirm diverse sexualities in their health education programs.

Using vignettes to evaluate the outcomes of student learning : data from the evaluation of the new social work degree in England

This paper reports the use of vignettes as a methodology to analyse the extent to which the new social work degree programmes enabled students to develop their analytical and reflective capabilities. Two vignettes, which focused on children and families and adult social care respectively, were developed for the study. Students were asked to respond in writing, from the perspective of a social worker, to a standard set of questions at the beginning (T1) and end of their degree programme (T2). Considering the responses to all questions across the two vignettes, a series of scales was developed to measure the key themes which had been identified by qualitative analysis. These included ‘Attention to process of relationships’ and ‘Social/structural/political awareness’. Responses were also rated as ‘descriptive’, ‘analytic’ or ‘reflective’.

Students from six universities in England participated. From an original sample of 222 students, it was possible to match 79 T1 and T2 responses. Analysis of variance demonstrated statistically significant increases in nine of the 11 themes and increases in ratings for analysis and reflection.

In conclusion, vignettes can be used to produce both qualitative and quantitative data in respect of changes in students’ acquisition of knowledge and skills over time.

Graph matching : fast candidate elimination using machine learning techniques

Graph matching is an important class of methods in pattern recognition. Typically, a graph representing an unknown pattern is matched with a database of models. If the database of model graphs is large, an additional factor in induced into the overall complexity of the matching process. Various techniques for reducing the influence of this additional factor have been described in the literature. In this paper we propose to extract simple features from a graph and use them to eliminate candidate graphs from the database. The most powerful set of features and a decision tree useful for candidate elimination are found by means of the C4.5 algorithm, which was originally proposed for inductive learning of classication rules. Experimental results are reported demonstrating that effcient candidate elimination can be achieved by the proposed procedure.

Empowering teachers through a professional learning program that focused on a representation intensive pedagogical approach

A representation-intensive pedagogical approach challenges students to generate and negotiate the representations (text, graphs, models, diagrams) that constitute the discursive practices of science, rather than focusing on the text-based, definitional versions of concepts. It thus represents a more active view of knowledge than traditional structural approaches. Previous research conducted on a small scale with a few topics and teachers, successfully demonstrated enhanced outcomes for students, in terms of sustained engagement with ideas, and quality learning, and for teachers enhanced pedagogical knowledge, and epistemological understanding. This paper explores the efficacy of embedding a representations-intensive pedagogical approach into a state-wide professional learning program that was delivered to secondary science teachers in Victoria, Australian, in 2010/2011. The professional learning program involved participating teachers undertaking two successive days of professional development, then completing a small classroom-based project in their schools before returning for the third day of professional development. The program was supported by on online drupal website. In determining the impact of the professional learning program on the teachers’ practice data was collected in the form of program participant surveys, presentations of the teachers’ classroom-based projects, focus group interviews and phone interviews.

Teaching critical reflection: a tool for transformative learning in social work?

In an Australian Bachelor of Social Work degree, critical reflection is a process explicitly taught in a fourth year subject to students who have returned from their first field placement experience in agencies delivering social work programmes. The purpose of teaching critical reflection is to enable social work students to become autonomous and critical thinkers who can reflect on society, the role of social work and social work practices. The way critical reflection is taught in this fourth year social work unit relates closely to the aims of transformative learning. Transformative learning aims to assist students to become autonomous thinkers. Specifically, the critical reflection process taught in this subject aims to assist students to recognise their own and other people's frames of reference, to identify the dominant discourses circulating in making sense of their experience, to problematise their taken-for -granted ‘lived experience’, to reconceptualise identity categories, disrupt assumed causal relations and to reflect on how power relations are operating. Critical reflection often draws on many theoretical frameworks to enable the recognition of current modes of thinking and doing. In this paper, we will draw primarily on how post-structural theories, specifically Foucault's theorising, disrupt several taken-for-granted concepts in social work.

Structural understanding from note-taking within video lectures

 Note-taking during all kinds of lectures is a standard and easy technique for learners of all ages to self-document their thought during learning. Based on a metacognitive rationale, this study investigates the effect of note-taking within different kinds of video-lectures – text-driven presentations versus graphical presentations. The availability of note-taking is experimentally controlled for 54 undergraduate students, and the quality of the nodes is then projected to the learning outcome as compared to the content of the lectures. Our results indicate little direct impact of the note quality, and contra-intuitively, not taking notes helped learners with their knowledge structure. Our study helps to understand the limits of note-taking during learning and with a broader theoretical understanding of idiosyncratic externalization.

Viewpoints, creativity and embodied learning : developing perceptual and kinaesthetic skills in non-dancers studying undergraduate university drama

Viewpoints are a structural approach to training and directing for theatre. Originating from the innovative, inventive and exploratory approach of Mary Overlie and the self- confessed scavenger approach of Anne Bogart, Viewpoints offers a practical philosophy of working. As a training approach it begins with a disciplined engagement  of the body in space and time. The tangible elements of the Viewpoints provide a set of tasks on which the student can focus, thus freeing the imagination and spirit to  create. Yet at the same time the systematic logic of Viewpoints supports novice practitioners to begin to question their perception, invest in creative practice that demands action and exploration, and to deconstruct, re-organise and rebuild scores and sequences in the pursuit of theatre that is visceral and visual. This essay reports on undergraduate student experiences of learning Viewpoints. It interrogates the demands of embodied learning of the movement/structural system on non- ancers and examines student-actor experiences of embodied learning from multiple of subject positions – observer/participant/creator/reflector/actor.  

Least squares learning and the US Treasury bill rate

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning. © 2014 Elsevier B.V.