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The Influence of a Co-operative Learning Strategy on Mathematics Test Results of Pre-technician Students at the Port Elizabeth Technikon

Developing Practice-Based Research for Co-Operation and Co-Operative Learning

Co-operative Learning

The Social and Intellectual Outcomes of Learning in Groups

This volume's coverage ranges across the educational spectrum, from pre-school years to university, and presents a comprehensive overview of this alternative educative approach; illustrating how co-operative learning experiences can promote socialization and friendships, and facilitate learning.

Co-operative learning from a curriculum perspective But what is a curriculum?
Walker (1990) described the curriculum in terms of content and purpose of an
educational programme together with its organization. Purpose Placing co-
operative ...

How to Improve Your Reading

Jane (Off): Why, hello, Snuffy. Billy isn't home. Mother: See, it's Snuffy Ginnis
come to see Billy. (Jane enters right followed by Snuffy Ginnis. He wears a plaid
cap just like Billy's and he carries a brown paper bag.) Jane: It's Snuffy Ginnis
and he ...

Annual Report of the Building Committee of the Cathedral of Saint Peter and Saint Paul

... Catharine M'Callom, Ann M'Attee, Ellen M'Gettigan, Margaret M'Grath, Charles
M'Swiggen, Michael M'Ginnis, James M'Geehan, Patrick M'Govern, James M'
Ginnis, Catharine M'Kane, Philip M'Kellin, Patrick Newland, Professor O'Conner,
 ...

Active Learning Techniques for Librarians

Practical Examples

A practical work outlining the theory and practice of using active learning techniques in library settings. It explains the theory of active learning and argues for its importance in our teaching and is illustrated using a large number of examples of techniques that can be easily transferred and used in teaching library and information skills to a range of learners within all library sectors. These practical examples recognise that for most of us involved in teaching library and information skills the one off session is the norm, so we need techniques that allow us to quickly grab and hold our learners’ attention. The examples are equally useful to those new to teaching, who wish to bring active learning into their sessions for the first time, as to those more experienced who want to refresh their teaching with some new ideas and to carry on their development as librarian teachers. Outlines the argument for more active learning techniques in our sessions Explains the theory of active learning Includes examples that can be used in teaching

but that people have other kinds of intelligences with which they are just as
capable of learning and excelling. For example, learners may have high spatial
intelligence with which they can relate and transfer information in picture or
image form ...

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.

14. 15. 16. 17. 18. 19. Ackley, H., Hinton, E., Sejnowski, J.: A learning algorithm
for boltzmann machines. Cognitive Science, 147–169 (1985) 2. Bengio, Y.:
Learning deep architectures for AI. Foundations and Trends in Machine Learning
2(1), ...

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

Gu, Q., Li, Z., Han, J.: Joint feature selection and subspace learning. In: IJCAI
Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, p.
1294 (2011) Han, L., Zhang, Y.: Learning multi-level task groups in multi-task
learning ...

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

0 0.5 1 1.5 2 2.5 3 3.5 Online Q-learning vs Always On Q-learning (Online)
Always On 0 20 40 60 80 100 120 140 A v e r a g e d a i l y c o s t ( E u r o ) Time (
Days) Fig. 2. Online Q-learning vs “Always on” control Offline Q-learning Comfort
 ...