48,803 research outputs found
Class tournament as an assessment method in physics courses : a pilot study
Testing knowledge is an integral part of a summative assessment at schools. It can be performed in many different ways. In this study we propose assessment of physics knowledge by using a class tournament approach. Prior to a statistical analysis of the results obtained over a tournament organized in one of Polish high schools, all its specifics are discussed at length, including the types of questions assigned, as well as
additional self- and peer-evaluation questionnaires, constituting an integral part of the tournament. The impact of the tournament upon student improvement is examined by confronting the results of a post-test with pre-tournament studentsâ achievements reflected in scores earned in former, tests written by the students in experimental group and their colleagues from control group. We also present some of studentsâ and teachersâ feedback on the idea of a tournament as a tool of assessment. Both the
analysis of the tournament results and the studentsâ and teachersâ opinions point to at least several benefits of our approach
Knowledge convergence in collaborative learning
In collaborative learning the question has been raised as to how learners in small groups influence one another and converge or diverge with respect to knowledge. Knowledge convergence can be conceptualised as knowledge equivalence and as shared knowledge prior to, during, and subsequent to collaborative learning. Knowledge equivalence refers to learners becoming more similar to their learning partners with regard to the extent of their individual knowledge. Shared knowledge means that learners have knowledge on the very same concepts as their learning partners. In this article, we provide measures for assessing both, knowledge equivalence and shared knowledge
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
200
A Method to Discover Digital Collaborative Conversations in Business Collaborations
Many companies have a suite of digital tools, such as Enterprise Social
Networks, conferencing and document sharing software, and email, to facilitate
collaboration among employees. During, or at the end of a collaboration,
documents are often produced. People who were not involved in the initial
collaboration often have difficulties understanding parts of its content
because they are lacking the overall context. We argue there is valuable
contextual and collaborative knowledge contained in these tools (content and
use) that can be used to understand the document. Our goal is to rebuild the
conversations that took place over a messaging service and their links with a
digital conferencing tool during document production. The novelty in our
approach is to combine several conversation-threading methods to identify
interesting links between distinct conversations. Specifically we combine
header-field information with social, temporal and semantic proximities. Our
findings suggest the messaging service and conferencing tool are used in a
complementary way. The primary results confirm that combining different
conversation threading approaches is efficient to detect and construct
conversation threads from distinct digital conversations concerning the same
document
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