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Climbing the Great Wall: Linking Teacher Beliefs and Learning Styles in Cross-Cultural Teaching - Observations from cross-cultural teaching in Mainland China
Scholars have suggested separate relationships between culture and learning styles, and between culture and teaching beliefs. In this essay I suggest that interrelated relationships between culture, learning styles and teacher beliefs may exist. Drawing on personal observations from cross-cultural teaching experiences in Mainland China, the essay illustrates how culture, learning styles and teacher beliefs inform each other and how they might be combined into an inclusive framework. Such a framework could aid in identifying and overcoming challenges from cross-cultural teaching and cross-cultural learning. The observations shed further light onto the on-going debate of how Chinese learn. The essay concludes with directions for future research for further development of the framework and our understanding of cross-cultural differences in the classroom
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks
How can we reuse existing knowledge, in the form of available datasets, when
solving a new and apparently unrelated target task from a set of unlabeled
data? In this work we make a first contribution to answer this question in the
context of image classification. We frame this quest as an active learning
problem and use zero-shot classifiers to guide the learning process by linking
the new task to the existing classifiers. By revisiting the dual formulation of
adaptive SVM, we reveal two basic conditions to choose greedily only the most
relevant samples to be annotated. On this basis we propose an effective active
learning algorithm which learns the best possible target classification model
with minimum human labeling effort. Extensive experiments on two challenging
datasets show the value of our approach compared to the state-of-the-art active
learning methodologies, as well as its potential to reuse past datasets with
minimal effort for future tasks
Stratified Transfer Learning for Cross-domain Activity Recognition
In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global domain shift while ignoring the intra-affinity between classes, which
will hinder the performance of the algorithms. In this paper, we propose a
novel and general cross-domain learning framework that can exploit the
intra-affinity of classes to perform intra-class knowledge transfer. The
proposed framework, referred to as Stratified Transfer Learning (STL), can
dramatically improve the classification accuracy for cross-domain activity
recognition. Specifically, STL first obtains pseudo labels for the target
domain via majority voting technique. Then, it performs intra-class knowledge
transfer iteratively to transform both domains into the same subspaces.
Finally, the labels of target domain are obtained via the second annotation. To
evaluate the performance of STL, we conduct comprehensive experiments on three
large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI
DSADS), which demonstrates that STL significantly outperforms other
state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%).
Furthermore, we extensively investigate the performance of STL across different
degrees of similarities and activity levels between domains. And we also
discuss the potential of STL in other pervasive computing applications to
provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready
version
Learning from Physics Education Research: Lessons for Economics Education
We believe that economists have much to learn from educational research
practices and related pedagogical innovations in other disciplines, in
particular physics education. In this paper we identify three key features of
physics education research that distinguish it from economics education
research - (1) the intentional grounding of physics education research in
learning science principles, (2) a shared conceptual research framework focused
on how students learn physics concepts, and (3) a cumulative process of
knowledge-building in the discipline - and describe their influence on new
teaching pedagogies, instructional activities, and curricular design in physics
education. In addition, we highlight four specific examples of successful
pedagogical innovations drawn from physics education - context-rich problems,
concept tests, just-in-time teaching, and interactive lecture demonstrations -
and illustrate how these practices can be adapted for economic education.Comment: 19 pages, 3 figures, submitted to Journal of Economic Education, also
available from Social Science Research Network
<http://ssrn.com/abstract=1151430
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