Effects of introducing an interactive teaching and learning activity (TLA) in the engineering classroom

This research study examines qualitatively and quantitatively the influence of introducing an activity in the traditional engineering classroom. It studies instances of active learning and its relationship with the student learning outcomes. The primary purpose of this study was to compare the learning outcomes of students who were involved in an active TLA with those students who were not, instead they learned under traditional teaching and studying approaches. I present the argument that the introduction of a TLA in class stimulates student engagement bringing enormous benefits to student learning. The outcomes of this study were measured using qualitative and quantitative data to evaluate the levels of student engagement, achievement and satisfaction in the terms of Intended Learning Outcomes (ILOs). Results indicate that students held positive attitude towards the activities in class and also, that a positive link between TLA, learning approach and learning outcome exist. It also provides insights about the potential benefits of active learning when compared with traditional, passive and teacher-centred methods of teaching & learning.

Collaborative learning and critical thinking in technology-enhanced environments : an instructional design framework

The nature and characteristics of how learners learn today are changing. As technology use in learning and teaching continues to grow, its integration to facilitate deep learning and critical thinking becomes a primary consideration. The implications for learner use, implementation strategies, design of integration frameworks and evaluation of their effectiveness in learning environments cannot be overlooked. This study specifically looked at the impact that technology-enhanced learning environments have on different learners’ critical thinking in relation to eductive ability, technological self-efficacy, and approaches to learning and motivation in collaborative groups. These were explored within an instructional design framework called CoLeCTTE (collaborative learning and critical thinking in technology-enhanced environments) which was proposed, revised and used across three cases. The field of investigation was restricted to three key questions: 1) Do learner skill bases (learning approach and eductive ability) influence critical thinking within the proposed CoLeCTTE framework? If so, how?; 2) Do learning technologies influence the facilitation of deep learning and critical thinking within the proposed CoLeCTTE framework? If so, how?; and 3) How might learning be designed to facilitate the acquisition of deep learning and critical thinking within a technology-enabled collaborative environment? The rationale, assumptions and method of research for using a mixed method and naturalistic case study approach are discussed; and three cases are explored and analysed. The study was conducted at the tertiary level (undergraduate and postgraduate) where participants were engaged in critical technical discourse within their own disciplines. Group behaviour was observed and coded, attributes or skill bases were measured, and participants interviewed to acquire deeper insights into their experiences. A progressive case study approach was used, allowing case investigation to be implemented in a "ladder-like" manner. Cases 1 and 2 used the proposed CoLeCTTE framework with more in-depth analysis conducted for Case 2 resulting in a revision of the CoLeCTTE framework. Case 3 used the revised CoLeCTTE framework and in-depth analysis was conducted. The findings led to the final version of the framework. In Cases 1, 2 and 3, content analysis of group work was conducted to determine critical thinking performance. Thus, the researcher used three small groups where learner skill bases of eductive ability, technological self-efficacy, and approaches to learning and motivation were measured. Cases 2 and 3 participants were interviewed and observations provided more in-depth analysis. The main outcome of this study is analysis of the nature of critical thinking within collaborative groups and technology-enhanced environments positioned in a theoretical instructional design framework called CoLeCTTE. The findings of the study revealed the importance of the Achieving Motive dimension of a student’s learning approach and how direct intervention and strategies can positively influence critical thinking performance. The findings also identified factors that can adversely affect critical thinking performance and include poor learning skills, frustration, stress and poor self-confidence, prioritisations over learning; and inadequate appropriation of group role and tasks. These findings are set out as instructional design guidelines for the judicious integration of learning technologies into learning and teaching practice for higher education that will support deep learning and critical thinking in collaborative groups. These guidelines are presented in two key areas: technology and tools; and activity design, monitoring, control and feedback.

A learner perspective on barriers to e-learning

This study aims to identify and categorize barriers to e-learning adoption and the relative impact of those barriers on learners. It contributes to the understanding of learner perceptions of barriers, the different types of barriers and their relative importance. This study used a quantitative methodology grounded in previous literature. The study is based on a self-administered questionnaire conducted with employees in a rail organisation. This research demonstrates there are three key factors that represent barriers to e-learning: the nature of e-learning as a learning approach, the use of technology, and concerns about lack of time and potential interruptions when trying to complete e-learning.

Adapting an argumentation framework for online discourse analysis : a knowledge building approach

The use of online tools to support teaching and learning is now commonplace within educational institutions, with many of these institutions mandating or strongly encouraging the use of a blended learning approach to teaching and learning. Consequently, these institutions generally adopt a learning management system (LMS), with a fixed set of collaborative tools, in the belief that effective teaching and learning approaches will be used, to allow students to build knowledge. While some studies into the use of an LMS’s still identify continued didactic approaches to teaching and learning, the focus of this paper is on the ability of collaborative tools such as discussion forums, to build knowledge. In the context of science education, argumentation is touted as playing an important role in this process of knowledge building. However, there is limited research into argumentation in other domains using online discussion and a blended learning approach. This paper describes a study, using design research, which adapts a framework for argumentation that can be applied to other domains. In particular it will focus on an adapted social argumentation schema to identify argument in a discussion forum of N=16 participants in a secondary High School.

An examination of learning design in elite springboard diving

The overarching aim of this programme of work was to evaluate the effectiveness of the existing learning environment within the Australian Institute of Sport (AIS) elite springboard diving programme. Unique to the current research programme, is the application of ideas from an established theory of motor learning, specifically ecological dynamics, to an applied high performance training environment. In this research programme springboard diving is examined as a complex system, where individual, task, and environmental constraints are continually interacting to shape performance. As a consequence, this thesis presents some necessary and unique insights into representative learning design and movement adaptations in a sample of elite athletes. The questions examined in this programme of work relate to how best to structure practice, which is central to developing an effective learning environment in a high performance setting. Specifically, the series of studies reported in the chapters of this doctoral thesis: (i) provide evidence for the importance of designing representative practice tasks in training; (ii) establish that completed and baulked (prematurely terminated) take-offs are not different enough to justify the abortion of a planned dive; and (iii), confirm that elite athletes performing complex skills are able to adapt their movement patterns to achieve consistent performance outcomes from variable dive take-off conditions. Chapters One and Two of the thesis provide an overview of the theoretical ideas framing the programme of work, and include a review of literature pertinent to the research aims and subsequent empirical chapters. Chapter Three examined the representativeness of take-off tasks completed in the two AIS diving training facilities routinely used in springboard diving. Results highlighted differences in the preparatory phase of reverse dive take-offs completed by elite divers during normal training tasks in the dry-land and aquatic training environments. The most noticeable differences in dive take-off between environments began during the hurdle (step, jump, height and flight) where the diver generates the necessary momentum to complete the dive. Consequently, greater step lengths, jump heights and flight times, resulted in greater board depression prior to take-off in the aquatic environment where the dives required greater amounts of rotation. The differences observed between the preparatory phases of reverse dive take-offs completed in the dry-land and aquatic training environments are arguably a consequence of the constraints of the training environment. Specifically, differences in the environmental information available to the athletes, and the need to alter the landing (feet first vs. wrist first landing) from the take-off, resulted in a decoupling of important perception and action information and a decomposition of the dive take-off task. In attempting to only practise high quality dives, many athletes have followed a traditional motor learning approach (Schmidt, 1975) and tried to eliminate take-off variations during training. Chapter Four examined whether observable differences existed between the movement kinematics of elite divers in the preparation phases of baulked (prematurely terminated) and completed take-offs that might justify this approach to training. Qualitative and quantitative analyses of variability within conditions revealed greater consistency and less variability when dives were completed, and greater variability amongst baulked take-offs for all participants. Based on these findings, it is probable that athletes choose to abort a planned take-off when they detect small variations from the movement patterns (e.g., step lengths, jump height, springboard depression) of highly practiced comfortable dives. However, with no major differences in coordination patterns (topology of the angle-angle plots), and the potential for negative performance outcomes in competition, there appears to be no training advantage in baulking on unsatisfactory take-offs during training, except when a threat of injury is perceived by the athlete. Instead, it was considered that enhancing the athletes' movement adaptability would be a more functional motor learning strategy. In Chapter Five, a twelve-week training programme was conducted to determine whether a sample of elite divers were able to adapt their movement patterns and complete dives successfully, regardless of the perceived quality of their preparatory movements on the springboard. The data indeed suggested that elite divers were able to adapt their movements during the preparatory phase of the take-off and complete good quality dives under more varied take-off conditions; displaying greater consistency and stability in the key performance outcome (dive entry). These findings are in line with previous research findings from other sports (e.g., shooting, triple jump and basketball) and demonstrate how functional or compensatory movement variability can afford greater flexibility in task execution. By previously only practising dives with good quality take-offs, it can be argued that divers only developed strong couplings between information and movement under very specific performance circumstances. As a result, this sample was sometimes characterised by poor performance in competition when the athletes experienced a suboptimal take-off. Throughout this training programme, where divers were encouraged to minimise baulking and attempt to complete every dive, they demonstrated that it was possible to strengthen the information and movement coupling in a variety of performance circumstances, widening of the basin of performance solutions and providing alternative couplings to solve a performance problem even when the take-off was not ideal. The results of this programme of research provide theoretical and experimental implications for understanding representative learning design and movement pattern variability in applied sports science research. Theoretically, this PhD programme contributes empirical evidence to demonstrate the importance of representative design in the training environments of high performance sports programmes. Specifically, this thesis advocates for the design of learning environments that effectively capture and enhance functional and flexible movement responses representative of performance contexts. Further, data from this thesis showed that elite athletes performing complex tasks were able to adapt their movements in the preparatory phase and complete good quality dives under more varied take-off conditions. This finding signals some significant practical implications for athletes, coaches and sports scientists. As such, it is recommended that care should be taken by coaches when designing practice tasks since the clear implication is that athletes need to practice adapting movement patterns during ongoing regulation of multi-articular coordination tasks. For example, volleyball servers can adapt to small variations in the ball toss phase, long jumpers can visually regulate gait as they prepare for the take-off, and springboard divers need to continue to practice adapting their take-off from the hurdle step. In summary, the studies of this programme of work have confirmed that the task constraints of training environments in elite sport performance programmes need to provide a faithful simulation of a competitive performance environment in order that performance outcomes may be stabilised with practice. Further, it is apparent that training environments can be enhanced by ensuring the representative design of task constraints, which have high action fidelity with the performance context. Ultimately, this study recommends that the traditional coaching adage 'perfect practice makes perfect", be reconsidered; instead advocating that practice should be, as Bernstein (1967) suggested, "repetition without repetition".

A CLASS act : the teaching team approach to subject coordination

Advancing the development of good practice around the teaching team has been the focus of a recently completed, nationally funded Australian grant entitled Coordinators Leading Advancement of Sessional Staff (CLASS). The project focused on developing leadership capacity of subject coordinators to provide supportive contexts for sessional staff to enhance their knowledge of teaching practice and contribute to subject improvement through a team approach. An action learning approach and notions of distributed leadership underpinned the activities of the teaching teams in the program. This paper provides an overview of a practical approach, led by the subject coordinator, to engaging sessional staff through the facilitation of a supportive network within the teaching team. It addresses some of the gaps identified in the recent literature which includes lack of role clarity for all members of the team and provides some examples of initiatives that teams engaged with to address some of the challenges identified. Resources to support this approach were developed and are shared through the project website. Recommendations for future direction include improved policy and practice at the institutional level, better recognition and reward for subject coordinators and resourcing to support the participation and professional development needs of sessional staff.

Applying threshold learning theory to teach sustainable business practice in post-graduate engineering education

This paper presents the results of a qualitative action-research inquiry into how a highly diverse cohort of post-graduate students could develop significant capacity in sustainable development within a single unit (course), in this case a compulsory component of four built environment masters programs. The method comprised applying threshold learning theory within the technical discipline of sustainable development, to transform student understanding of sustainable business practice in the built environment. This involved identifying a number of key threshold concepts, which once learned would provide a pathway to having a transformational learning experience. Curriculum was then revised, to focus on stepping through these targeted concepts using a scaffolded, problem-based-learning approach. Challenges included a large class size of 120 students, a majority of international students, and a wide span of disciplinary backgrounds across the spectrum of built environment professionals. Five ‘key’ threshold learning concepts were identified and the renewed curriculum was piloted in Semester 2 of 2011. The paper presents details of the study and findings from a mixed-method evaluation approach through the semester. The outcomes of this study will be used to inform further review of the course in 2012, including further consideration of the threshold concepts. In future, it is anticipated that this case study will inform a framework for rapidly embedding sustainability within curriculum.

External knowledge and query strategies in active learning: A study in clinical information extraction

This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that 1) amongst existing query strategies, the ones based on the classification model’s confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and 2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.

Effectiveness of on-line resources to enhance student learning and engagement

BACKGROUND For engineering graduates to be work-ready with marketable skills they must not only be well-versed with engineering science and its applications, but also able to adapt to using commercial software that is widely used in engineering practice. Hydrological/hydraulic modelling is one aspect of engineering practice which demands the ability to apply fundamentals into design and construction using software. The user manuals for such software are usually tailored for the experienced engineer but not for undergraduates who typically are novices to concepts of modelling and software tools. As the focus of a course such as Advanced Water Engineering is on the wider aspects of engineering application of hydrological and hydraulic concepts, it is ineffective for the lecturers to direct the students to user manuals as students have neither the time nor the desire to sift through numerous pages in a manual. An alternative and efficient way to demonstrate the use of the software is enabling students to develop a model to simulate real-world scenario using the tools of the software and directing them to make informed decisions based on outcomes. PURPOSE Past experience of the lecturer showed that the resources available for the students left a knowledge gap leading to numerous student queries outside contact hours. The purpose of this study is to assess how effective purpose-built video resources can be in supplementing the traditional learning resources to enhance student learning. APPROACH Short-length animated video clips comprising guided step-by-step instructions were prepared using screen capture software to capture screen activity and later edited to focus on specific features using pop-up annotations; Vocal narration was purposely excluded to avoid disturbances due to noise and allow different learning paces of individual students. The video clips were made available to the students alongside the traditional resources/approaches such as in-class demonstrations, guideline notes, and tips for efficient and error-free procedural descriptions. The number of queries the lecturer received from the student cohort outside the lecture times was recorded. An anonymous survey to assess the usefulness and adequacy of the courseware was conducted. OUTCOMES While a significant decline in the number of student queries was noted, an overwhelming majority of the survey respondents confirmed the usefulness of the purpose-developed courseware. CONCLUSIONS/RECOMMENDATIONS/SUMMARY The survey and lecturer’s experience indicated that animated demonstration video clips illustrating the various steps involved in developing hydrologic and hydraulic models and simulating design scenarios is an effective supplement for traditional learning resources. Among the many advantages of the custom-made video clips as a learning resource are that they (1) highlight the aspects that are important to undergraduate learning but not available in the software manuals as the latter are designed for more mature users/learners; (2) provide short, to-the point communication in a step-by-step manner; (3) allow students flexibility to self-learn at their own pace; (4) enhance student learning; and (5) enable time savings for the lecturer in the long term by avoiding queries of a repetitive nature. It is expected that these newly developed resources will be improved to incorporate students’ suggestions before being offered to future cohorts of students. The concept can also be expanded to other relevant courses where animated demonstrations of key modelling steps are beneficial to student learning.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Benefits of blended learning for the first year medical imaging students in preparation for clinical placement

Although there is a plethora of definitions of blended learning, the underlying distinguishing feature is the combination of traditional content delivery and the utilisation of technology. Within Medical Imaging undergraduate education there is evidence of advantages and increased student engagement when utilising a blended learning approach. Although the embedding of technology has been proven to be a useful teaching tool, “Educators should tailor their teaching media to learner’s needs rather than assume that web based learning is intrinsically superior”. This study aims to determine which clinical learning tools are perceived to be the most useful to the student in preparing them for placements.

Pedagogical tools designed to promote engagement and active learning in the biomedical sciences and allied health disciplines

Perceiving students, science students especially, as mere consumers of facts and information belies the importance of a need to engage them with the principles underlying those facts and is counter-intuitive to the facilitation of knowledge and understanding. Traditional didactic lecture approaches need a re-think if student classroom engagement and active learning are to be valued over fact memorisation and fact recall. In our undergraduate biomedical science programs across Years 1, 2 and 3 in the Faculty of Health at QUT, we have developed an authentic learning model with an embedded suite of pedagogical strategies that foster classroom engagement and allow for active learning in the sub-discipline area of medical bacteriology. The suite of pedagogical tools we have developed have been designed to enable their translation, with appropriate fine-tuning, to most biomedical and allied health discipline teaching and learning contexts. Indeed, aspects of the pedagogy have been successfully translated to the nursing microbiology study stream at QUT. The aims underpinning the pedagogy are for our students to: (1) Connect scientific theory with scientific practice in a more direct and authentic way, (2) Construct factual knowledge and facilitate a deeper understanding, and (3) Develop and refine their higher order flexible thinking and problem solving skills, both semi-independently and independently. The mindset and role of the teaching staff is critical to this approach since for the strategy to be successful tertiary teachers need to abandon traditional instructional modalities based on one-way information delivery. Face-to-face classroom interactions between students and lecturer enable realisation of pedagogical aims (1), (2) and (3). The strategy we have adopted encourages teachers to view themselves more as expert guides in what is very much a student-focused process of scientific exploration and learning. Specific pedagogical strategies embedded in the authentic learning model we have developed include: (i) interactive lecture-tutorial hybrids or lectorials featuring teacher role-plays as well as class-level question-and-answer sessions, (ii) inclusion of “dry” laboratory activities during lectorials to prepare students for the wet laboratory to follow, (iii) real-world problem-solving exercises conducted during both lectorials and wet laboratory sessions, and (iv) designing class activities and formative assessments that probe a student’s higher order flexible thinking skills. Flexible thinking in this context encompasses analytical, critical, deductive, scientific and professional thinking modes. The strategic approach outlined above is designed to provide multiple opportunities for students to apply principles flexibly according to a given situation or context, to adapt methods of inquiry strategically, to go beyond mechanical application of formulaic approaches, and to as much as possible self-appraise their own thinking and problem solving. The pedagogical tools have been developed within both workplace (real world) and theoretical frameworks. The philosophical core of the pedagogy is a coherent pathway of teaching and learning which we, and many of our students, believe is more conducive to student engagement and active learning in the classroom. Qualitative and quantitative data derived from online and hardcopy evaluations, solicited and unsolicited student and graduate feedback, anecdotal evidence as well as peer review indicate that: (i) our students are engaging with the pedagogy, (ii) a constructivist, authentic-learning approach promotes active learning, and (iii) students are better prepared for workplace transition.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

The Google Online Marketing Challenge: A Transnational Comparison of Classroom Learning with Real Clients, Real Money, and Real Advertising Campaigns

In 2008, a collaborative partnership between Google and academia launched the Google Online Marketing Challenge (hereinafter Google Challenge), perhaps the world’s largest in-class competition for higher education students. In just two years, almost 20,000 students from 58 countries participated in the Google Challenge. The Challenge gives undergraduate and graduate students hands-on experience with the world’s fastest growing advertising mechanism, search engine advertising. Funded by Google, students develop an advertising campaign for a small to medium sized enterprise and manage the campaign over three consecutive weeks using the Google AdWords platform. This article explores the Challenge as an innovative pedagogical tool for marketing educators. Based on the experiences of three instructors in Australia, Canada and the United States, this case study discusses the opportunities and challenges of integrating this dynamic problem-based learning approach into the classroom.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.