Volume 9, 2021: Issue 1

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Title:

A cross-language study of speech recognition systems for English, German, and Hebrew

Author(s):

Vered Silber Varod, The Open University of Israel, Israel

Ingo Siegert, Otto von Guericke University Magdeburg, Germany

Oliver Jokisch, Leipzig University of Telecommunications, Germany

Yamini Sinha, Otto von Guericke University Magdeburg, Germany

Nitza Geri, The Open University of Israel, Israel

Abstract:

The ability to replay selected video segments is a major advantage of online video lectures. Replay is a learning instance that reflects active engagement. This paper develops the ‘replay-peak attention chart’ as a new performance measure of learner’s attention, based on the control chart concept, which is used for Statistical Process Control (SPC) in operations management. This study follows the design science research paradigm and employs a mixed methods methodology, combining quantitative learning analytics with qualitative analysis of notable segment replay instances by viewers of online video lectures. An analysis of a successful Massive Open Online Course (MOOC), titled “Negotiation Management” provides a proof-of-concept for the replay-peak attention chart, as a visual heuristic tool for identifying notable learning instances. The MOOC includes Educational Entertainment (edutainment) in the form of negotiation simulations which are presented as sitcoms, and are meant to increase learner engagement. From an attention economy perspective, the replay-peak attention chart may help instructors and designers to focus their limited attention resources on segments of online video lecture sessions that may require pedagogical interventions. This paper critically discusses the replay-peak attention chart conceptualization and its initial proof-of-concept. It suggests future research directions for substantiating the replay-peak attention chart, and investigating the effect of edutainment on online learning. The replay-peak attention chart is a dynamic descriptive performance measure, which has a promising potential to improve the design of effective online video lectures as an e-learning resource.

Keywords:

Automatic Speech Recognition (ASR), performance measures, speech-recognition evaluation metrics, ASR engine, cross-language, genre, error rate

DOI:

https://doi.org/10.36965/OJAKM.2021.9(1)1-15

Type:

Research paper

Journal:

The Online Journal of Applied Knowledge Management (OJAKM), ISSN: 2325-4688

Publisher:

International Institute for Applied Knowledge Management (IIAKM)

Received:

1 March 2021

Revised:

10 May 2021

Accepted:

13 May 2021

Accepting Editor:

Meir Russ

Pages:

1-15