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.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Reinforcement Learning approaches to Economic Dispatch problem

This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions and a 20 generator system considering transmission losses. A comparison of the computation times of different algorithms is also carried out.

Cross-curricular approaches to teaching and learning.

Para profesores de primaria y todos aquellos involucrados en el diseño curricular. Aporta ideas prácticas sobre cómo el maestro puede utilizar la historia y la geografía como materias transversales para proporcionar un vehículo a través del cual los niños pueden aplicar los conocimientos y conceptos adquiridos en estas áreas. Además, los niños toman conciencia de cómo utilizar, desarrollar y ampliar los conocimientos que están adquiriendo de modo que el aprendizaje puede ser más integrado y relevante para ellos.

Inclusive approaches to teaching and learning mathematics

This chapter explores the role of mentors in supporting pre-service teachers to include all children in mathematics teaching, no matter what their individual needs.

Exploring three approaches to offer distance learning courses through a social network of health researchers in three African countries

The Institute of Tropical Medicine in Antwerp hereby presents the results of two pilot distance learning training programmes, developed under the umbrella of the AFRICA BUILD project (FP7). The two courses focused on evidence-based medicine (EBM): with the aim of enhancing research and education, via novel approaches and to identify research needs emanating from the field. These pilot experiences, which were run both in English-speaking (Ghana), and French-speaking (Mali and Cameroon) partner institutions, produced targeted courses for the strengthening of research methodology and policy. The courses and related study materials are in the public domain and available through the AFRICA BUILD Portal (http://www.africabuild.eu/taxonomy/term/37); the training modules were delivered live via Dudal webcasts. This paper assesses the success and difficulties of transferring EBM skills with these two specific training programmes, offered through three different approaches: fully online facultative courses, fully online tutor supported courses or through a blended approach with both online and face-to-face sessions. Key factors affecting the selection of participants, the accessibility of the courses, how the learning resources are offered, and how interactive online communities are formed, are evaluated and discussed.

Approaches to Organization of Teaching for Creating Digital Learning Materials

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

Alternative approaches to education: Tolstoy’s thinking on teaching and learning and its relevance for today

In this article I reflect upon the educational writings and teaching experiences of the 19th-Century Russian novelist Leo Tolstoy. Tolstoy is known to have attached much importance to his own writing on education, even more than to the literary creations for which he is best remembered. His writings on education have much to contribute to our present-day understanding of the learning process and cover such issues as, ‘learner autonomy’, ‘motivation’, ‘relationship’ and ‘student voice’. Tolstoy’s teaching experience was with multiethnic peasant children in his schools in Yasnaya Polyana. I intend to illustrate that the themes and issues that arose from his experiences in the 1860s can still find resonance with students and teachers in the 21st century.

Digital serious games and simulation games - comparison of two approaches to lean training

In an increasingly competitive and globalized world, companies need effective training methodologies and tools for their employees. However, selecting the most suitable ones is not an easy task. It depends on the requirements of the target group (namely time restrictions), on the specificities of the contents, etc. This is typically the case for training in Lean, the waste elimination manufacturing philosophy. This paper presents and compares two different approaches to lean training methodologies and tools: a simulation game based on a single realistic manufacturing platform, involving production and assembly operations that allows learning by playing; and a digital game that helps understand lean tools. This paper shows that both tools have advantages in terms of trainee motivation and knowledge acquisition. Furthermore, they can be used in a complementary way, reinforcing the acquired knowledge.

Using mobile device detection approaches to augment the accuracy of web delivery content

Recent studies of mobile Web trends show a continuous explosion of mobile-friendly content. However, the increasing number and heterogeneity of mobile devices poses several challenges for Web programmers who want to automatically get the delivery context and adapt the content to mobile devices. In this process, the devices detection phase assumes an important role where an inaccurate detection could result in a poor mobile experience for the enduser. In this paper we compare the most promising approaches for mobile device detection. Based on this study, we present an architecture for a system to detect and deliver uniform m-Learning content to students in a Higher School. We focus mainly on the devices capabilities repository manageable and accessible through an API. We detail the structure of the capabilities XML Schema that formalizes the data within the devices capabilities XML repository and the REST Web Service API for selecting the correspondent devices capabilities data according to a specific request. Finally, we validate our approach by presenting the access and usage statistics of the mobile web interface of the proposed system such as hits and new visitors, mobile platforms, average time on site and rejection rate.

Approaches to OER Development

OER development is becoming more sophisticated as instructors and course specialists become more familiar with the environment. Most OER development approaches for online courses have been developed from those that were appropriate in the face-to-face context. However, the OER online environment opens up new possibilities for learning as well as holding particular limitations. This paper presents some approaches that OER implementers should bear in mind when initiating and supporting OER course development projects.1. Beg, borrow, or steal courseware. Don't reinvent the wheel.2. Take what exists and build the course around it.3. Mix and match. Assemble. Don't create.4. Avoid the "not invented here" syndrome. 5. Know the content -garbage in and garbage out.6. Establish deadlines. Work to deadlines, but don't be unrealistic. 7. Estimate your costs and then double them. Double them again. 8. Be realistic in scheduling and scoping.9. The project plan must be flexible. Be prepared for major shifts.10. Build flexibly for reuse and repurposing -generalizability reduces costs 11. Provide different routes to learning. 12. Build to international standards.There are necessary features in every OER, including introduction, schedule etc. but it is most important to keep the course as simple as possible. Extreme Programming (XP) methodology can be adapted from software engineering to aid in the course development process.

Open Preservation: Community Approaches to Ensuring Long-Term Access to Digital Collections

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014