Search results for "e learning"
showing 10 items of 2703 documents
Diversity in random subspacing ensembles
2004
Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. It was shown experimentally and theoretically that in order for an ensemble to be effective, it should consist of classifiers having diversity in their predictions. A number of ways are known to quantify diversity in ensembles, but little research has been done about their appropriateness. In this paper, we compare eight measures of the ensemble diversity with regard to their correlation with the accuracy improvement due to ensembles. We conduct experiments on 21 data sets from the UCI machine learning repository, comparing the correlations for random subspacing ensembles with diffe…
Individualizing deep dynamic models for psychological resilience data
2020
ABSTRACTDeep learning approaches can uncover complex patterns in data. In particular, variational autoencoders (VAEs) achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project (MARP), which features intermittent longitudinal measurements of stressors and mental health, we propose an approach for individualized, dynamic modeling in this latent space. Specifically, we utilize ordinary differential equations (ODEs) and develop a novel technique for obtaining person-specific ODE parameters even in settings with a rather small number of individuals and observations, incomplete data, an…
Implicit learning
2008
International audience; All of us have learned much about language, music, physical or social environment, and other complex domains, out of any intentional attempts to acquire information. This chapter describes first how studies investigating this form of learning in laboratory situations have shifted from a rule-based interpretation to interpretations assuming a progressive tuning to the statistical regularities of the environment. The next section examines the potential of statistical learning, and whether statistical learning stems from statistical computations or chunk formation. Then the acceptations in which this form of learning may be qualified as implicit are analysed. Finally, i…
Vectors of Pairwise Item Preferences
2019
Neural embedding has been widely applied as an effective category of vectorization methods in real-world recommender systems. However, its exploration of users’ explicit feedback on items, to create good quality user and item vectors is still limited. Existing neural embedding methods only consider the items that are accessed by the users, but neglect the scenario when a user gives high or low rating to a particular item. In this paper, we propose Pref2Vec, a method to generate vector representations of pairwise item preferences, users and items, which can be directly utilized for machine learning tasks. Specifically, Pref2Vec considers users’ pairwise item preferences as elementary units. …
Artificial Decision Maker Driven by PSO : An Approach for Testing Reference Point Based Interactive Methods
2018
Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization f…
Challenges for the Teacher's Role in Promoting Productive Knowledge Construction in Computer-Supported Collaborative Learning Contexts
2010
This chapter discusses challenges related to teachers’ pedagogical activities in facilitating productive discussions among students in Computer-Supported Collaborative Learning (CSCL) contexts. In the light of two different cases from secondary-level and higher education contexts, the authors examine how teachers’ pedagogical choices influenced the quality of students’ activity, namely Web-based discussion. The results of our studies indicated that rich moments of collaboration were rare and distributed unequally among the students. The obvious weakness from the perspective of teachers’ pedagogical activities was that in neither of the studies was the students’ interaction in the discussion…
Khmer character recognition using artificial neural network
2014
Character Recognition has become an interesting and a challenge topic research in the field of pattern recognition in recent decade. It has numerous applications including bank cheques, address sorting and conversion of handwritten or printed character into machine-readable form. Artificial neural network including self-organization map and multilayer perceptron network with the learning ability could offer the solution to character recognition problem. In this paper presents Khmer Character Recognition (KCR) system implemented in Matlab environment using artificial neural networks. The KCR system described the utilization of integrated self-organization map (SOM) network and multilayer per…
Innovative Teaching Strategies in Accounting
2018
Accounting in higher education is a practical subject and teaching financial accounting has nowadays become a challenge. Professional organizations and corporate employers prefer to hire students with critical thinking skills, communication skills, technical skills, and analytical skills. Accounting students often have a negative attitude towards the subject and struggle to understand core concepts of accounting standards. Finding and using new and innovative methods of teaching accounting is a crucial skill for teachers. Certain methods and approaches can truly enhance the learning process, and done right, applying innovative learning and attention-management techniques to classes is a win…
SMEs' heterogeneity at the extensive margin and within the intensive margin of trade
2021
In this paper, we contribute to the literature on firm-heterogeneity and trade, by looking not only at the firm-level determinants of trade participation (i.e. extensive margin) but also at differences between firms with different levels of trade intensity (i.e. intensive margin). Further, we compare firms that are born ‘local’ and display different scales of international exposure to firms that are born ‘global’, i.e. access international markets soon after their birth. Using a large World Bank dataset of SMEs from 112 countries and qualitative dependent variable models, our analysis uncovers the heterogeneity of SMEs not only at the extensive margin but also within the intensive margin of…
Handling local concept drift with dynamic integration of classifiers : domain of antibiotic resistance in nosocomial infections
2006
In the real world concepts and data distributions are often not stable but change with time. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the target concept. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined. In this paper we consider the use of an ensemble integration technique that helps to better handle concept drift at t…