Predicting post-training readiness to work with computers: The predominance of log-based variables

Dalit Mor; Hagar Laks; Arnon Hershkovitz

doi:10.1145/2723576.2723656

Predicting post-training readiness to work with computers: The predominance of log-based variables

Dalit Mor, Hagar Laks, Arnon Hershkovitz

School of Education

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In today's job market, computer skills are part of the prerequisites for many jobs. In this paper, we report on a study of readiness to work with computers (the dependent variable) among unemployed women (N=54) after participating in a unique training focused on computer skills and empowerment. Associations were explored between this variable and 17 variables from four categories: log-based, computer literacy and experience, job-seeking motivation and practice, and training satisfaction. Only two variables were associated with the dependent variable: Knowledge post-test duration and satisfaction with content. Building a prediction model of the dependent variable, another feature was highlighted: Total number of actions in the course website along the course. Our analyses highlight the predominance of the log-based variables over the variables from the other categories, and we thoroughly discuss this finding.

Original language	English
Title of host publication	Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015
Publisher	Association for Computing Machinery
Pages	400-401
Number of pages	2
ISBN (Electronic)	9781450334174
DOIs	https://doi.org/10.1145/2723576.2723656
State	Published - 16 Mar 2015
Event	5th International Conference on Learning Analytics and Knowledge, LAK 2015 - Poughkeepsie, United States Duration: 16 Mar 2015 → 20 Mar 2015

Publication series

Name	ACM International Conference Proceeding Series
Volume	16-20-March-2015

Conference

Conference	5th International Conference on Learning Analytics and Knowledge, LAK 2015
Country/Territory	United States
City	Poughkeepsie
Period	16/03/15 → 20/03/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 5 Gender Equality

Keywords

Decision Tree
Work Readiness
Working With Computers

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/2723576.2723656

Cite this

Mor, D., Laks, H., & Hershkovitz, A. (2015). Predicting post-training readiness to work with computers: The predominance of log-based variables. In Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015 (pp. 400-401). (ACM International Conference Proceeding Series; Vol. 16-20-March-2015). Association for Computing Machinery. https://doi.org/10.1145/2723576.2723656

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Mor, D, Laks, H & Hershkovitz, A 2015, Predicting post-training readiness to work with computers: The predominance of log-based variables. in Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015. ACM International Conference Proceeding Series, vol. 16-20-March-2015, Association for Computing Machinery, pp. 400-401, 5th International Conference on Learning Analytics and Knowledge, LAK 2015, Poughkeepsie, United States, 16/03/15. https://doi.org/10.1145/2723576.2723656

Predicting post-training readiness to work with computers: The predominance of log-based variables. / Mor, Dalit; Laks, Hagar; Hershkovitz, Arnon.
Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015. Association for Computing Machinery, 2015. p. 400-401 (ACM International Conference Proceeding Series; Vol. 16-20-March-2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Laks, Hagar

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M3 - منشور من مؤتمر

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Predicting post-training readiness to work with computers: The predominance of log-based variables

Abstract

Publication series

Conference

UN SDGs

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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