Developing Pre-Service Teachers’ Evidence-Based Argumentation skills on Socio-Scientific Issues

We report on a study of the effect of meta-level awareness on the use of evidence in discourse. The participants were 66 pre-service teachers who were engaged in a dialogic activity. Meta-level awareness regarding the use of evidence in discourse was heightened by having same-side peers collaborating in arguing on the computer against successive pairs of peers on the opposing side of an issue on the topic of Climate Change and by engaging in explicit reflective activities on the use of evidence. Participants showed significant advances both in their skill of producing evidence-based arguments and counterarguments and regarding the accuracy of the evidence used. Advances were also observed at the meta-level, reflecting at least implicit understanding that using evidence is an important goal of argumentation. Another group of pre-service teachers, who studied about the role of evidence in science in the context of regular curriculum and served as a control condition, did not exhibit comparable advances in the use of evidence in argumentation. Educational implications are discussed

Online Tensor Methods for Learning Latent Variable Models

We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles. We consider decomposition of moment tensors using stochastic gradient descent. We conduct optimization of multilinear operations in SGD and avoid directly forming the tensors, to save computational and storage costs. We present optimized algorithm in two platforms. Our GPU-based implementation exploits the parallelism of SIMD architectures to allow for maximum speed-up by a careful optimization of storage and data transfer, whereas our CPU-based implementation uses efficient sparse matrix computations and is suitable for large sparse datasets. For the community detection problem, we demonstrate accuracy and computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic modeling problem, we also demonstrate good performance on the New York Times dataset. We compare our results to the state-of-the-art algorithms such as the variational method, and report a gain of accuracy and a gain of several orders of magnitude in the execution time.Comment: JMLR 201

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Classroom expriments on project management communication

This manuscript gives a brief overview of three sets of experiments in the classroom with students following a Project Management (PM) course module using a blended learning approach. The impact of communication on the student performance using business games as well as the advantages of the use of integrative case studies and their impact on the learning experience of these students are tested. The performance of students is measured by their quantitative output on the business game or case exercise, while their learning experience is measured by the student evaluations. The experiments have been carried out on a sample of students with a different background, ranging from university students with or without a strong quantitative background but no practical experience, to MBA students at business schools and PM professionals participating in a PM training. The results have been presented at an international workshop on computer supported education in Lisbon (Portugal) in 2015 and details have been published in Vanhoucke and Wauters (2015)

Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construction. Students (N = 87) were first randomly assigned to the two information presentation conditions to work individually on a case-based assignment in IDLM. Students who received information during learning task clusters tended to show better results on knowledge construction than those who received information only before each cluster. The students within the two separate information presentation conditions were then randomly assigned to pairs to discuss the outcomes of their assignments under either the personal discussion or online discussion condition in CSCL. When supportive information had been presented before each learning task cluster, online discussion led to better results than personal discussion. When supportive information had been presented during the learning task clusters, however, the online and personal discussion conditions had no differential effect on knowledge construction. Online discussion in CSCL appeared to compensate for suboptimal timing of presentation of supportive information before the learning task clusters in IDLM

Pilot Evaluation of the Mexican Model of Dual TVET in the State of Mexico

Since the first public announcement of the Mexican Model of Dual TVET (MMFD) in June 2013, more than 5,000 apprentices have enrolled in the programme and around 2,000 already graduated. The Ministry of Education (SEP and CONALEP), the Chambers of Commerce (i.e. COPARMEX) and the German Cooperation Agencies (i.e. CAMEXA) have been collaborating with state authorities, families, schools and companies to turn this initial idea into a significant and sustainable initiative. Although the numbers are still small, it seemed necessary to undertake a pilot evaluation study of the implementation and impact of this program on its participants to inform those responsible for this policy. We decided to focus our study on the State of Mexico because of the higher number of apprentices in this state and because of the access that the CONALEP authorities gave us to the informants. The report that you are about to read is structured in four main sections. In the first one we reviewed the international evidence on the experiences of policy transfer of Dual TVET. Transferring international good practice sin TVET is always a complex process that requires careful attention to the experiences and lessons from those that tried to do it before. In the second section, we present the main characteristics of the Mexican Model of Dual TVET and the specificities of its implementation in the State of Mexico. In a federal country like Mexico, it is important to understand that national policies may largely vary across states in terms of design and implementation. The third section outlines the methodology of the study, which is inspired by the realist evaluation principles. Realist evaluation, not only tries to measure the impact of interventions on beneficiaries, but also to understand the causal mechanisms that explain why this policy is more effective in certain contexts and with certain beneficiary populations than in others. In the final section, the results of the interviews and the survey with 25 apprentices that completed their studies under the MMFD in the State of Mexico are presented. Obviously, the reduced sample of the study limits the representativeness of our findings but it will offer some expected and unexpected results that should not be ignored by those involved in this policy in the State of Mexico and nationally