Leveraging students' handwritten notes to link to watched instructional videos
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Jaddigadde Srinivasa, Ranjitha
issued: 2021-04-28T20:48:53Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Handwritten note-taking with pen and paper is still the preferred medium to achieve information seeking and comprehension from diverse learning objects. But students, especially in video-based learning settings, exercise laborious practices to re-find the corresponding video context when reviewing notes. We propose the orchestration of students' handwritten notebook content as interoperable links to retrieve previously watched instructional videos. This work articulates the research objectives in two phases. In phase 1, we analyzed the characteristic features of notebook content of watched videos. And, in phase 2, we investigated student expectations and requirements of the proposed video retrieval system. Analysis of quality handwritten notebook samples and the related video materials in a lab study with ten engineering students revealed distinctive characteristic representations of note content such as text, formula, figures, and chiefly a hybrid of all the 3. A box plot interpretation of notes and the watched video content confirmed that at least 75% of the identified note samples demonstrated a verbatim overlap of 50% or more with the related video content, hinting at its potential use as a query artifact. Additionally, the video references to collected note samples exhibited referencing at three temporal levels: point, interval, and whole video. A 12-student lab study indicated higher satisfaction for video matches returned at the `interval' level and showcased students' existing workarounds for linking back to videos. Overall, students rated a positive Mean score for the system's usability to re-find note-specific video context. A medium-fidelity prototype was built, using off-the-shelf computer vision algorithms, to deduce technology requirements associated with the proposed approach. When tested on the 181 identified note samples, the prototype system matched 77.5% of the samples to corresponding watched videos. The proposed method worked exceptionally well …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/78040
note: Leveraging Students’ Handwritten Notes to link to watchedInstructional VideosbyRanjitha Jaddigadde SrinivasaB.E., Sapthagiri College of Engineering, 2014a thesis submitted in partial fulfillmentof the requirements for the degree ofMaster of Applied ScienceinThe Faculty of Graduate and Postdoctoral Studies(Electrical and Computer Engineering)The University of British Columbia(Vancouver)April 2021© Ranjitha Jaddigadde Srinivasa, 2021The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Leveraging Students’ Handwritten Notes to link to watched InstructionalVideossubmitted by Ranjitha Jaddigadde Srinivasa in partial fulfillment of the require-ments for the degree of Master of Applied Science in Electrical and ComputerEngineering.Examining Committee:Dr. Sidney Fels, Department of Electrical and Computer EngineeringSupervisorDr. Dongwook Yoon, Department of Computer ScienceSupervisory Committee memberDr. Julia Rubin, Department of Electrical and Computer EngineeringSupervisory Committee memberiiAbstractHandwritten note-taking with pen and paper is still the preferred medium to achieveinformation seeking and comprehension from diverse learning objects. But students, es-pecially in video-based learning settings, exercise laborious practices to re-find the corre-sponding video context when reviewing notes. We propose the orchestration of students’handwritten notebook content as interoperable links to retrieve previously watched in-structional videos. This work articulates the research objectives in two phases. In phase1, we analyzed the characteristic features of notebook content of watched videos. And,in phase 2, we investigated student expectations and requirements of the proposed videoretrieval system.Analysis of quality handwritten notebook samples and the related video materials in alab study with ten engineering students revealed distinctive characteristic representati…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0397020
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/78040