Building the foundations of professional expertise: creating a dialectic between work and formal learning

Recent critiques of management and teacher education curricula and teaching pay particular attention to the disconnection between the de-contextualised, formal knowledge and analytical techniques conveyed in university programs and the messy, ill-structured nature of practice. At the same time research into professional expertise suggests that its development requires bringing together different forms of knowledge and the integration of formal and non-formal learning with the development of cognitive flexibility. Such complex learning outcomes are unlikely to be achieved through a 'knowledge transmission' approach to curriculum design. In this article we argue that in many ways current higher education practices create barriers to developing ways of knowing which can underpin the formation of expertise. Using examples from two practice-focused distance learning courses, we explore the role of distance learning in enabling a dialogue between academic and workplace learning and the use of 'practice dialogues' among course participants to enable integration of learning experiences. Finally, we argue that we need to find ways in higher education of enabling students to engage in relevant communities of expertise, rather than drawing them principally into a community of academic discourse which is not well aligned with practice

Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Case studies of personalized learning

Deliverable 4.1, Literature review of personalised learning and the Cloud, started with an evaluation and synthesis of the definitions of personalized learning, followed by an analysis of how this is implemented in a method (e-learning vs. i-learning, m-learning and u-learning), learning approach and the appropriate didactic process, based on adapted didactic theories. From this research a list of criteria was created needed to implement personalised learning onto the learner of the future. This list of criteria is the basis for the analysis of all case studies investigated. – as well to the learning process as the learning place. In total 60 case studies (all 59 case studies mentioned in D6.4 Education on the Cloud 2015 + one extra) were analysed. The case studies were compared with the list of criteria, and a score was calculated. As a result, the best examples could be retained. On average most case studies were good on: taking different learning methods into account, interactivity and accessibility and usability of learning materials for everyone. All had a real formal education content, thus aiming at the core-curriculum, valuing previous knowledge, competences, life and work skills, also informal. Also the availability of an instructor / tutor or other network of peers, experts and teachers to guide and support the learning is common. On the other hand, most case studies lack diagnostics tests as well at the start (diagnostic entry test), during the personalized learning trajectory and at the end (assessment at the end). Also most do not include non-formal and informal learning aspects. And the ownership of personalized learning is not in the hands of the learner. Five of the 60 case studies can as a result be considered as very good examples of real personalized learning

Solving Bongard Problems with a Visual Language and Pragmatic Reasoning

More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems. These problems are now known as Bongard problems. Although they are well known in the cognitive science and AI communities only moderate progress has been made towards building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing complex visual concepts based on this vocabulary. Using this language and Bayesian inference, complex visual concepts can be induced from the examples that are provided in each Bongard problem. Contrary to other concept learning problems the examples from which concepts are induced are not random in Bongard problems, instead they are carefully chosen to communicate the concept, hence requiring pragmatic reasoning. Taking pragmatic reasoning into account we find good agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself. While this approach is far from solving all Bongard problems, it solves the biggest fraction yet

Middle-out approaches to reform of university teaching and learning: Champions striding between the top-down and bottom-up approaches

In recent years, Australian universities have been driven by a diversity of external forces, including funding cuts, massification of higher education, and changing student demographics, to reform their relationship with students and improve teaching and learning, particularly for those studying off-campus or part-time. Many universities have responded to these forces either through formal strategic plans developed top-down by executive staff or through organic developments arising from staff in a bottom-up approach. By contrast, much of Murdoch University's response has been led by a small number of staff who have middle management responsibilities and who have championed the reform of key university functions, largely in spite of current policy or accepted practice. This paper argues that the "middle-out" strategy has both a basis in change management theory and practice, and a number of strengths, including low risk, low cost, and high sustainability. Three linked examples of middle-out change management in teaching and learning at Murdoch University are described and the outcomes analyzed to demonstrate the benefits and pitfalls of this approach

Learning Model Transformations from Examples using FCA: One for All or All for One?

International audienceIn Model-Driven Engineering (MDE), model transformations are basic and primordial entities. An efficient way to assist the definition of these transformations consists in completely or partially learning them. MTBE (Model Transformation By-Example) is an approach that aims at learning a model transformation from a set of examples, i.e. pairs of transformation source and target models. To implement this approach, we use Formal Concept Analysis as a learning mechanism in order to extract executable rules. In this paper, we investigate two learning strategies. In the first strategy, transformation rules are learned independently from each example. Then we gather these rules into a single set of rules. In the second strategy, we learn the set of rules from all the examples. The comparison of the two strategies on the well-known transformation problem of class diagrams to relational schema showed that the rules obtained from the two strategies are interesting. Besides the first one produces rules which are more proper to their examples and apply well compared to the second one which builds more detailed rules but larger and more difficult to analyze and to apply

Upaya Guru Dalam Mengembangkan Pemahaman Konsep Kepada Anak Didik Dalam Pembahasan Trigonometri

UPAYA GURU DALAM MENGEMBANGKAN PEMAHAMAN KONSEP KEPADA ANAK DIDIK DALAM PEMBAHASAN TRIGONOMETRI ?é?á Abstrak Concepts are ideas that can be used to categorize or classify objects, whether a particular objects is an example of the concepts or not. At the simplest level we often observe objects with their characteristic. Based on the observation that the similarities can be seen from the object, so we can classify these objects. Concept math learning can be done by using a deductive approach begins by giving definitions, axioms, and the theorems followed by giving examples. This example can be given by the teacher or found by students. Deductive approach in teaching mathematics is commonly practiced. Learning by using deductive approach have to do fastly so it caan be more efficient. If a math lesson conducted with formal approach, but its implementation is deviate from the formal system, so it use informal approach. In this approach, theorem or formulas of mathematics is given. Then used to solve the problem without degrading or prove prior learning with informal approach can be used to train the students to discover and prove the characteristic or formula

The Statistical Physics of Learning Revisited:Typical Learning Curves in Model Scenarios

The exchange of ideas between computer science and statistical physics has advanced the understanding of machine learning and inference significantly. This interdisciplinary approach is currently regaining momentum due to the revived interest in neural networks and deep learning. Methods borrowed from statistical mechanics complement other approaches to the theory of computational and statistical learning. In this brief review, we outline and illustrate some of the basic concepts. We exemplify the role of the statistical physics approach in terms of a particularly important contribution: the computation of typical learning curves in student teacher scenarios of supervised learning. Two, by now classical examples from the literature illustrate the approach: the learning of a linearly separable rule by a perceptron with continuous and with discrete weights, respectively. We address these prototypical problems in terms of the simplifying limit of stochastic training at high formal temperature and obtain the corresponding learning curves.</p

Meeting the growing demand for engineers and their educators: the potential for open and distance learning

As with all teaching, open and distance approaches are successful only if based on good pedagogical design addressing the purpose, structure and pace of the material, hence engaging students and encouraging active learning. For distance learning such pedagogical design is often expensive, and can only be justified by comparatively large student numbers. Much open and distance teaching offers meagre student support. To be successful, course developers must integrate student support into the learning materials, including such elements as a modest number of face-to-face sessions or electronic communication at a distance. This presentation discusses these issues in the context of SET distance teaching and presents examples of good practice from the UKOU, including: • an introductory course in ICT that adopts an issues-based approach, in order to de-mystify the subject and make it more attractive to students • resource-based approaches in engineering education • team projects at a distance • an emphasis on ‘active learning’ An argument is also to be made for the importance of openness if we really wish to promote engineering. In this context ‘openness’ means making programmes available to all students (even those without formal school-leaving qualifications) that will ultimately enable them to qualify as a professional engineer or an educator of engineers. The traditional approach to engineering education has been hierarchical and linear: a good school leaving certificate in mathematics / science followed by an often very theoretical university education plus an application-oriented final project. If we are serious about attracting new engineers, this will no longer do. An open and distance approach to engineering formation, based on outcomes rather than input educational levels, and with an emphasis on lifelong learning and professional development, can make a major contribution to chang

Implications on Rural Adult Learning in the Absence of Broadband Internet

The purpose of this study was to establish a deeper understanding of the educational needs of rural-based learners within the context of online learning opportunities. It was hoped to ascertain whether rural learner’s needs differ in terms of learning choices from that of their urban counterparts. The basis for the urban examples is based totally upon available literature. This study is particularly interested in identifying predictors for why rural learners choose to participate in online based adult and community education using a case study approach. Seven themes were identified during this study and are presented as a model for potential predictors of formal and informal online learning in rural communities