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Journal Issue
Volume 9 Issue 1, ECEL 2010 special issue / Apr 2011  pp1‑114

Editor: Carlos Vaz de Carvalho

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An Automated Individual Feedback and Marking System: An Empirical Study  pp1‑14

Trevor Barker

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Semi‑Automatic Grading of Students’ Answers Written in Free Text  pp15‑22

Nuno Escudeiro, Paula Escudeiro, Augusto Cruz

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Enhanced Approach of Automatic Creation of Test Items to foster Modern Learning Setting  pp23‑38

Christian Gutl, Klaus Lankmayr, Joachim Weinhofer, Margit Hofler

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Methodology for Evaluating Quality and Reusability of Learning Objects  pp39‑51

Eugenijus Kurilovas, Virginija Bireniene, Silvija Serikoviene

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Fluidity in the Networked Society ‑ Self‑initiated learning as a Digital Literacy Competence  pp52‑62

Karin Tweddell Levinsen

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Discovering Student Web Usage Profiles Using Markov Chains  pp63‑74

Alice Marques, Orlando Belo

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Assessing Student Transitions in an Online Learning Environment  pp75‑86

Minoru Nakayama, Hiroh Yamamoto

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Abstract

Assessment surveys of students are often conducted in order to evaluate online learning activities. Most surveys measure responses to questions which are based on students’ subjective impressions. The purpose of this study is to examine participants’ assessments made during the transitional phase in an online learning environment which includes blended and fully online courses at a Japanese national university. Students were enrolled in two‑unit Master’s or Bachelor’s degree courses which were taught by the same professor. The total number of students with valid survey data was 184 (92 Masters, 67 Bachelors for the blended learning course and 25 Bachelors for the fully online course). A survey questionnaire consisting of 10 questions measured the self‑assessments of students’ online learning experiences. Three factors were extracted. There are no significant differences in all factor scores between the beginnings and the ends of the courses. These results show the coherence of students' assessments during the course. The correlation coefficients of the first factor scores (e‑learning evaluation) between the beginnings and the ends of the courses are not high, however (Masters: r=0.35, Bachelors for blended learning: r=0.46, and Bachelors for fully online: r=0.33). Therefore, some participants have changed their evaluations between the two surveys. When the differences in factor scores from the initial and final surveys are compared between students who rated the course highly at the beginning (high raters) and students who did not (low raters), the scores for the high raters decrease and the scores for the low raters increase. Also, the relationships between students’ transitions and the metrics of their behaviour were investigated. 

 

Keywords: online learning, student assessment, assessment in transition, blended learning, fully online learning

 

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Science, Sport and Technology ‑ a Contribution to Educational Challenges  pp87‑97

Kelly O’Hara, Paula Reis, Dulce Esteves, Rui Brás, Luísa Branco

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From Soap Opera to Research Methods Teaching: Developing an Interactive Website/DVD to Teach Research in Health and Social Care  pp98‑104

Abigail Sabey, Sue Horrocks

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A Different Vision in eLearning: Metaphors  pp105‑114

Nazime Tuncay, Ioana Andreea Stanescu, Mustafa Tuncay

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