With the paradigm shift to student-centered learning, the physical teaching space is being examined The configuration of classrooms, the technology within them, and the behaviors they encourage are frequently represented as a barrier to enacting student-centered teaching methods, because traditionally designed rooms typically lack flexibility in seating arrangement, are configured to privilege a speaker at the front of the room, and lack technology to facilitate student collaboration. But many colleges and universities are redesigning the spaces in which students learn, collapsing traditional lecture halls and labs to create new, hybrid spaces—large technology-enriched studios—with the flexibility to support active and collaborative learning in larger class sizes. With this change, our classrooms are coming to embody the 21st-century pedagogy which many educators accept, and research and teaching practice are beginning to help us to understand the educational implications of thoughtfully engineered classrooms—in particular, that space and how we use it affects what, how, and how much students learn. This is the 137th volume of this Jossey-Bass higher education series. It offers a comprehensive range of ideas and techniques for improving college teaching based on the experience of seasoned instructors and the latest findings of educational and psychological researchers.
5 This chapter reveals how thoughtful course redesign that specifically addresses
thephysical environment of a learning space cansignificantly improve student
learning. Pedagogy Matters, Too: The Impact of Adapting Teaching Approaches
to ...
This book constitutes the refereed proceedings of the Second International Workshop on Cooperative Information Systems, CIA'98, held in cognition with Agents World in July 1998 in Paris. The book presents nine invited contributions together with 14 revised full papers selected from a total of 54 submissions. The book is divided in parts on systems and applications; issues of design, querying, and communication; rational cooperation and electronic commerce; adaptive and collaborative information gathering; and mobile information agents in the internet.
This concerns, e.g., the use of efficient techniques from machine learning,
evolutionary computing, and symbolic or numerical approaches for uncertain
reasoning. Moreover, commercial aspects of information gathering in the Internet
are ...
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), ...
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 ...
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
In the general (non-parameterized) case, or when the prior “decorrelates” the re-
ward in different states, we do not expect active learning to bring a significant
advantage. We are currently conducting further experiments to gain a clearer ...
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
...
