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Supplemental Mobile Learner Support Through Moodle-Independent Assessment Bots

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Advances in Web-Based Learning – ICWL 2021 (ICWL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13103))

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Abstract

Conducting assessments is necessary to evaluate student performance. Online tests offer a scaling solution over traditional tests administered by examiners. However, these online tests come with the drawback of losing the interactivity that one has with examiners, as online tests mostly take the form of self-correcting tests which expect students to answer all available questions in a survey-like manner with feedback that may be given at the end. In this work, we present the implementation and evaluation of social bots capable of performing online tests. These bots are implemented as chatbots that can access and evaluate existing tests from learning management systems. With these bots, we enhance mobile learning support for students. Students can perform assessments anytime, anywhere using their favorite messenger application such as Rocket.Chat, Slack or Telegram. Furthermore, the activity data in the chat flows into learning record stores which can be aggregated and visualized there. These types of bots can be used as additional learning opportunities in university courses. As before, instructors can create their tests in their regular environment and make them automatically accessible to the bot, so that students can take the quiz in their learning management system, but also in a familiar chat environment. Our evaluation shows that assessment chatbots are an attractive and accessible self-assessment opportunity for students on a mobile device.

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Notes

  1. 1.

    https://www.statista.com/statistics/483255/.

  2. 2.

    https://tech4comp.de/.

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Acknowledgments

The authors would like to thank the German Federal Ministry of Education and Research (BMBF) for their kind support within the project “Personalisierte Kompetenzentwicklung durch skalierbare Mentoringprozesse” (tech4comp) under the project id 16DHB2110.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Alexander Tobias Neumann, Aaron David Conrardy & Ralf Klamma

Authors
  1. Alexander Tobias Neumann
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  2. Aaron David Conrardy
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  3. Ralf Klamma
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Corresponding author

Correspondence to Alexander Tobias Neumann .

Editor information

Editors and Affiliations

  1. University of Macau, Macau, China

    Wanlei Zhou

  2. University of Macau, Macau, China

    Yi Mu

Appendices

Appendix

A Extracting Content of Quizzes From Moodle

Moodle currently possesses a variety of RESTful API function calls which allows for content to be extracted. To extract the quiz content, following API calls are made with JSON as the content format (in calling order):

  1. 1.

    core_course_get_contents: Used to get the quizzes for a given course.

  2. 2.

    mod_quiz_start_attempt: Used to start a quiz on the Moodle platform.

  3. 3.

    mod_quiz_process_attempt: Used to stop the quiz attempt.

  4. 4.

    mod_quiz_get_attempt_review: Used to get HTML code of the review page.

Note that for each call an authentication token called wstoken is needed, which is provided by an admin of the moodle instance. The first function call returns every activity or resource contained in a course based on the given course id. The Assessment Handler then uses the response to find out the id for the chosen quiz topic. The second function call is used to choose and start a quiz attempt on the Moodle platform. As wstokens are linked to a specific account, the attempt starts on the respective account. The last two calls allow us to stop the started attempt and retrieve the HTML code of the final review page of the quiz. This review page contains every question that is in the quiz, the question types, the corresponding correct answers to each question, the optional feedback, and the marks corresponding to the questions. The jsoup library was used to parse the retrieved HTML code in Java and thus extract the quiz information using DOM methods provided by the library.

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Neumann, A.T., Conrardy, A.D., Klamma, R. (2021). Supplemental Mobile Learner Support Through Moodle-Independent Assessment Bots. In: Zhou, W., Mu, Y. (eds) Advances in Web-Based Learning – ICWL 2021. ICWL 2021. Lecture Notes in Computer Science(), vol 13103. Springer, Cham. https://doi.org/10.1007/978-3-030-90785-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-90785-3_7

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  • Online ISBN: 978-3-030-90785-3

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