Search results for " learning"

The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability

2020

In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, three dimensions are taken into account: agreement (that is, how much a group of raters mutually agree on a single case)

Language Learning Methodology for Adults: A Study of Linguistic Transfer

2014

Abstract The purpose of the present research is to bring together the evidence on transfer in adult L2 and L3 language acquisition and investigate the use and the relationship between languages in contact. The role of linguistic transfer ( Odlin, 1989 ) i.e. the imposition of previously learned patterns onto a new learning situation, has a facilitation or inhibition effect on the learner's progress in mastering a new language (L2 or L3). Our findings reveal that the cross-linguistic influence occurs both from the direction of the L2 to the L3 and from the L3 to the L2 ( Odlin, 2003 , Jarvis and Pavlenko, 2008 ). In the case of our participants, in the acquisition of L2 as the foreign langua…

Automation Inner Speech as an Anthropomorphic Feature Affecting Human Trust: Current Issues and Future Directions

2021

This paper aims to discuss the possible role of inner speech in influencing trust in human–automation interaction. Inner speech is an everyday covert inner monolog or dialog with oneself, which is essential for human psychological life and functioning as it is linked to self-regulation and self-awareness. Recently, in the field of machine consciousness, computational models using different forms of robot speech have been developed that make it possible to implement inner speech in robots. As is discussed, robot inner speech could be a new feature affecting human trust by increasing robot transparency and anthropomorphism.

Diversity in random subspacing ensembles

2004

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. It was shown experimentally and theoretically that in order for an ensemble to be effective, it should consist of classifiers having diversity in their predictions. A number of ways are known to quantify diversity in ensembles, but little research has been done about their appropriateness. In this paper, we compare eight measures of the ensemble diversity with regard to their correlation with the accuracy improvement due to ensembles. We conduct experiments on 21 data sets from the UCI machine learning repository, comparing the correlations for random subspacing ensembles with diffe…

Discovering the Factors Influencing Students' Perception of the Educational Environment in the Context of Foreign Language Training in a Technical Hi…

2010

Students’ Redesign of Mandatory Assignments in Teacher Education

2017

This article explores specific aspects of literacy practices in teacher education in Norway, building upon data collected within the research project Digital literacy and use of learning resources in teacher education in Norway (DigiGLU). Our main aim is to explore how teachers in different subject courses in teacher education (TE) design mandatory assignments, and how students respond to these designs. After the extensive TE-reform in 2010, in revised plans and documents guiding professional training, mandatory assignments (both form and content) were considered more important for the students’ learning process. In our investigation, the concepts of design for learning and design in learni…

Individualizing deep dynamic models for psychological resilience data

2020

ABSTRACTDeep learning approaches can uncover complex patterns in data. In particular, variational autoencoders (VAEs) achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project (MARP), which features intermittent longitudinal measurements of stressors and mental health, we propose an approach for individualized, dynamic modeling in this latent space. Specifically, we utilize ordinary differential equations (ODEs) and develop a novel technique for obtaining person-specific ODE parameters even in settings with a rather small number of individuals and observations, incomplete data, an…

What Factors have an Influence on A Quality Teaching Practice in Sciences?

2012

Abstract This paper aims to know to what extent in-service teachers who have not participated in science teaching training courses neither have performed any research in this field develop a quality teaching practice and what factors have influenced to achieve it. Questionnaires and non-participating observation protocols about the classroom work have been used as well as semi-structured interviews with a sample of teachers to obtain data. These instruments try to characterize the teaching action and determine what factors and in which degrees have influenced the performance of a quality science teaching activity.

Student Performance Prediction Based on Blended Learning

2021

Contribution: This article explored blended learning by implementing a student-centered teaching method based on the flipped classroom and small private online course (SPOC). The impact of general online learning behavior on student performance was analyzed. This work is practical and provides enlightenment for learning analysis and individualized teaching in blended learning. Background: Providing individualized teaching in a large class is an effective way to improve teaching quality, but the traditional teaching method makes it difficult for teachers to learn about each student’s learning situation. Blended learning offers the possibility of individualized teaching for teachers. The comb…

Implicit learning

2008

International audience; All of us have learned much about language, music, physical or social environment, and other complex domains, out of any intentional attempts to acquire information. This chapter describes first how studies investigating this form of learning in laboratory situations have shifted from a rule-based interpretation to interpretations assuming a progressive tuning to the statistical regularities of the environment. The next section examines the potential of statistical learning, and whether statistical learning stems from statistical computations or chunk formation. Then the acceptations in which this form of learning may be qualified as implicit are analysed. Finally, i…