Exploring Instructor Perceptions of Using Video-Based Feedback: A Review of the Literature

Abstract

The use of video feedback in face-to-face, blended, and online learning classes has increased markedly since 2014. However, the use of this form of feedback is not well understood. In this study, we conducted a systematic literature review of how higher education instructors perceive video-based feedback. We analyzed 39 peer-reviewed articles from 2009 to 2019 and identified four themes related to creating videos, the quality of feedback, connecting with students, and sustaining the practice of offering video-based feedback. Overall, most instructors claimed that creating video feedback was relatively easy and time-efficient to create. However, some instructors faced specific challenges related to recording, unwieldy software tools, and feeling anxious when creating videos. Instructors also noted that videos provided more detailed, higher-quality feedback. Additionally, instructors remarked that video feedback increased personal connections with their students. Finally, research on the long-term sustainability of providing video-based feedback was mixed.

Full text

Journal of Educational Informatics (2022), 3(1), 3-20.
Exploring Instructor Perceptions of Using Video-Based Feedback:
A Review of the Literature
TIMOTHY BAHULA AND ROBIN KAY
University of Ontario Institute of Technology
timothy.bahula@ontariotechu.net
robin.kay@ontariotechu.ca
The use of video feedback in face-to-face, blended, and online learning classes has
increased markedly since 2014. However, the use of this form of feedback is not well
understood. In this study, we conducted a systematic literature review of how higher
education instructors perceive video-based feedback. We analyzed 39 peer-reviewed
articles from 2009 to 2019 and identified four themes related to creating videos, the
quality of feedback, connecting with students, and sustaining the practice of offering
video-based feedback. Overall, most instructors claimed that creating video feedback
was relatively easy and time-efficient to create. However, some instructors faced
specific challenges related to recording, unwieldy software tools, and feeling anxious
when creating videos. Instructors also noted that videos provided more detailed, higher-
quality feedback. Additionally, instructors remarked that video feedback increased
personal connections with their students. Finally, research on the long-term
sustainability of providing video-based feedback was mixed.
Keywords: video feedback, feedback, assessment, higher education, systematic review
INTRODUCTION
High-quality feedback, essential for learning, involves communicating gaps between
desired outcomes and students’ performance (Carless, 2006). Effective feedback needs to move
from providing information and to motivating students to action (Mahoney et al., 2018). Hattie &
Timperley (2007) synthesized over 500 meta-analyses and identified feedback as a critical
determinant of student achievement. The review also suggested that the feedback quality and
impact varied significantly (Hattie & Timperley, 2007). To understand the effect of this variability,
educators and researchers need to conduct a more detailed analysis of the quality and format of
feedback.
Before the arrival of computer technology, instructors provided handwritten feedback
(Sommers, 1989), regularly leading to student disappointment and discouragement (C. Glover &

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Brown, 2006; Semke, 1984; Weaver, 2006). With the advent of computers, feedback morphed into
digital prose (Parkin et al., 2012; Ryan et al., 2019). Digital text, while easier to read (I. Glover et
al., 2015; Hepplestone et al., 2011; Price et al., 2010), did not address problems associated with
limited detail (Pitt & Norton, 2017), lack of pedagogical training (Richards et al., 2017), difficulty
students had connecting grades, feedback, and assessment criteria (I. Glover et al., 2015) or
negative emotions evoked from receiving feedback (Shields, 2015).
High-speed internet access, streaming services such as YouTube and Vimeo, and a growing
number of easy-to-use screencasting software tools have significantly increased video creation and
consumption (Henderson & Phillips, 2015). Accordingly, video-based feedback has emerged as a
viable format that can make feedback more detailed, personalized, and usable while addressing
specific challenges noted with handwritten and text-based formats (Ryan et al., 2019). Many
researchers have noted that higher education students prefer video over text-based feedback
(Cunningham, 2019; Ghosn-Chelala & Al-Chibani, 2018; Hall et al., 2016; Letón et al., 2018).
Students perceived video-based feedback to be more personal (Hall et al., 2016; Orlando, 2016),
detailed (Ghosn-Chelala & Al-Chibani, 2018; Mayhew, 2017), clear (Ali, 2016; Hall et al., 2016),
engaging (Edwards et al., 2012; Thompson & Lee, 2012), and stimulating higher-order thinking
(Lamey, 2015; Silva, 2012). Furthermore, video-based feedback improved revisions (Özkul &
Ortaçtepe, 2017), increased writing quality (Ali, 2016; Moore & Filling, 2012), and resulted in
higher grades (Alvira, 2016; Denton, 2014).
Although students embrace video-based feedback, we need to explore instructors’
perspectives. If instructors view video-based feedback as too difficult or time-consuming to create
or ineffective, they are unlikely to adopt this format. The purpose of the current study was to
investigate instructors’ perceptions about creating and employing video-based feedback in higher
education.
DEFINITIONS
Text-Based Feedback
In this literature review, we operationally define text-based feedback as a method of
providing guidance, commentary, support, and assessment to students using handwritten or typed
text. Traditionally, instructors hand wrote symbols, codes, and comments using a red pen (Semke,
1984), also referred to as handwritten markup feedback. Digital markup is defined similarly but
with the feedback provided in digital form. The use of Microsoft Word or Google Docs comments
and the “track changes” feature is a common method used to create digital markup (Chang et al.,
2018).
Video-Based Feedback
We identified four types of video-based feedback in this review: video, screencast, pencast,
and VoiceThread. Video feedback is operationally defined as a type of video-based feedback
consisting of a video of an instructor talking that has been recorded with a camera. Typically, the
camera frames the head and shoulders of the instructor (Henderson & Phillips, 2015), thereby
leading to an alternative label of a “talking head” video (Mahoney et al., 2018).
Screencast feedback comprises a computer screen recording while an instructor narrates
and performs actions such as highlighting, formatting, text insertions, and deletions (Chang et al.,
2018). Screencasts sometimes include an embedded video of an instructor.

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Pencast feedback is similar to screencast feedback. Rather than typing, drawing and writing
are recorded using a graphics tablet (e.g., Wacom or Bamboo) and digital pen (e.g., Livescribe)
while the instructor talks. Pencasts are commonly used in problem-solving scenarios such as those
seen in Khan Academy instructional videos.
Finally, VoiceThread feedback provides video-based feedback using the VoiceThread
platform. VoiceThread is a cloud application that allows users to upload media files (e.g.,
documents, presentations, and images) and other users to comment on these media files using text,
audio, or video. While VoiceThread could be used to provide only digital markup, it also offers
the possibility of delivering audio and video feedback by attaching clips to specific pieces of text.
METHOD
Overview
We conducted a systematic review of the literature on instructor video-based feedback
using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)
framework (Liberati et al., 2009). The PRISMA approach, originating in the field of medicine,
focuses on screening, analyzing, and synthesizing peer-reviewed articles to produce a
comprehensive, reliable review (Gough & Thomas, 2016). In the screening phase, we established
basic eligibility criteria, tested search terms, reviewed abstracts for relevance, and scanned
reference lists of eligible articles for additional sources. We then conducted a full-text review of
prospective articles to confirm eligibility. Finally, we analyzed eligible articles through careful
reading, extracting characteristics, coding emergent themes, and describing methodologies.
Synthesis of results included aggregating quantitative data about the articles and configuring
qualitative results from the articles (Gough & Thomas, 2016).
Eligibility Criteria
Establishing the eligibility criteria occurs early in the PRISMA process. We used the
following eligibility criteria to access articles for this systematic review: use of an audiovisual
medium to provide assessment feedback in educational contexts, participants studying or teaching
in higher education, original research and reported in an English-language, peer-reviewed journal
from 2009 to 2019. Conference papers, dissertations, and book chapters were excluded from this
review, as were studies focusing on video feedback to improve a skill (e.g., golf swing, surgical
technique, or teaching ability).
Information Sources
We used three primary information sources to search for articles. First, we searched the
following electronic databases: ProQuest’s The Summon Service, Education Source via
EBSCOhost, ERIC via EBSCOhost, LearnTechLib, PsycINFO, Academic Search Premier, and
Scholar Portal Journals. Each of these searches was restricted to higher education contexts.
Second, we examined 11 top educational journals based on h5-index and h5-median metrics for
education, educational technology, or online education as calculated by Google Scholar. These
journals included Computers & Education, British Journal of Educational Technology, Journal of
Computer Assisted Learning, Turkish Online Journal of Distance Education, Australasian Journal

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of Education Technology, The Internet and Higher Education, Canadian Journal of Learning and
Technology, Computers in Human Behavior, Journal of Educational Computing Research, Journal
of Educational Technology and Society and Assessment & Evaluation in Higher Education.
Finally, we used Google Scholar and ResearchGate to locate recently published or in-press articles
and to reveal any missing articles using other sources.
Search Strategy
Our initial search strategy used the keywords “screencast feedback” AND “assessment”
and yielded six articles. We modified the search using “screencast feedback OR video feedback
AND assessment,” increasing the number of articles to 346. After scanning the first ten articles,
we refined the search process by excluding keywords that introduced different areas of focus,
specifically: coaching, autism, intervention, medical education, and video analysis. We used this
final keyword phrasing for all databases. Finally, we employed a snowball strategy (Greenhalgh
& Peacock, 2005), scanning references for relevant articles.
Final Article Selection
We selected articles from the search procedures by scanning titles, abstracts, and, when
available, keywords and abstract excerpts. For articles that seemed potentially relevant, we
scanned the contents of the whole article based on established eligibility criteria to assess inclusion
in the review. The PRISMA framework produced 39 peer-reviewed articles.
Data Analysis
To provide an overall context for this review, we analyzed all articles based on key
descriptors, including the year of publication, country, academic level, academic discipline,
assessment type, media used, and feedback length. Next, we identified emerging themes by closely
reading the results and discussion sections and noting key findings. We used open coding on a
convenience sample of five current, relevant, high-quality, empirical articles selected early in the
analysis process. We then employed a constant comparative method on the remaining articles to
review codes for consistency and alignment with emerging themes (Corbin & Strauss, 2008).
Overall Context
The 39 articles reviewed in this study were published from 2009 to 2019, with a majority
published since 2014. Most papers originated from the United States, the United Kingdom and
Australia and focused on higher education faculty in education, language learning, the humanities,
business or STEM. Instructors in these studies used video-based feedback to provide formative
and summative assessments. The principal media used to provide feedback included screencasts
and instructor videos. The average video-based feedback recording lasted seven minutes and
ranged from two to 26 minutes.

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RESULTS AND DISCUSSION
Creation of Video-Based Feedback
Mathisen (2012) reported that the process of creating video-based feedback was intuitive
for instructors. Many studies reported that providing video-based feedback, reuired less time than
providing written feedback (Denton, 2014; Gonzalez & Moore, 2018; Griffiths & Graham, 2010;
Henderson & Phillips, 2015; Lamey, 2015; Mathisen, 2012) or the same amount of time (Crook et
al., 2012; Jones et al., 2012; O’Malley, 2011; Schilling & Estell, 2014; Vincelette & Bostic, 2013;
West & Turner, 2016). Gonzalez & Moore (2018) found that relying solely on video feedback
reduced time spent on each submission compared to written comments. Henderson & Phillips
(2015) reported that creating videos took, on average, half the amount of time as annotating
submissions. In Mathisen’s study (2012), one instructor asserted that providing video-based
feedback took a quarter of the time usually spent providing feedback and was much less prone to
misunderstanding. In Jones et al.’s study (2012), another instructor observed that video-based
feedback took no more time than text-based feedback but provided more feedback and saved time
in one-on-one meetings about the assignment.
Factors that improved efficiency in creating video-based feedback included one-take
recording (Anson, 2015; Hall et al., 2016; Lamey, 2015; Moore & Filling, 2012), familiarity with
the software (Hyde, 2013), and a conscious effort to be concise (Lamey, 2015; Mathisen, 2012).
While one-take recording improved efficiency, Anson (2015) cautioned that comment quality
might suffer if instructors were inexperienced. Hyde (2013) reported that the first few video-
feedback recordings took longer than providing text-based feedback. However, recordings became
much faster after becoming familiar with the software and annotating on screen. Both Lamey
(2015) and Mathisen (2012) found that the five-minute limit imposed by their software choice
forced them to be more concise when providing video-based feedback, yielding greater efficiency.
Mathisen (2012) initially took extensive notes and recorded a five-minute video but found the
grading process took longer than before. Reducing the target length to four minutes and eliminating
detailed note-taking resulted in more disciplined feedback and increased efficiency of feedback
creation (Mathisen, 2012). Beyond the efficiency of creating the feedback, video-based feedback
made face-to-face follow-up meetings more focused and efficient (Jones et al., 2012; Robinson et
al., 2015). The rich content of the screencasts that students received encouraged them to think of
meaningful questions and prepare for in-person meetings (Robinson et al., 2015).
Not all instructors found video feedback easy or efficient to create (Borup et al., 2015;
Mathieson, 2012). Some key problem areas included finding a quiet recording environment (Borup
et al., 2015), excessive attention to attire, grooming, and surroundings (Lamey, 2015), editing
video and screencast comments (Borup et al., 2015), performance anxiety (Parton et al., 2010;
Soden, 2017; Vincelette & Bostic, 2013), the type of software used to record videos (Silva, 2012;
Soden, 2016), and the distribution of video-based feedback (Borup et al., 2015). A quiet recording
environment was difficult to find for graduate students who did not have an office. Instructors who
recorded feedback at home occasionally did so at night when the house was quiet enough (Borup
et al., 2015). Conversely, text-based feedback was easier to provide wherever the instructor
happened to be, especially with a tablet (Borup et al., 2015). While Lamey (2015) never considered
getting fully dressed or shaving before providing marginal comments, video-based feedback made
that much more important. Instructors in the study of Borup et al. (2015) reported that the difficulty
of editing video caused them to rerecord after making an error, sometimes reducing the number of
comments included when rerecording.

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Quantity and Quality of Video-Based Feedback
Research on the total quantity of video-based feedback was positive. Several studies
reported that instructors were more verbose when providing video-based feedback than when using
text-based feedback. Denton (2014) noted that one minute of video created in that study contained
on average 135 words, compared to an average typing speed of 30 words per minute. As a result,
Denton (2014) concluded that instructors could provide more feedback in less time when using
video. Further, an analysis of feedback artifacts in four studies showed that instructors provided
two to five times as many words in video-based feedback compared to text-based feedback (Borup
et al., 2015; Elola & Oskoz, 2016; Henderson & Phillips, 2015; Thomas et al., 2017). In the study
by Henderson & Phillips (2015), instructors provided feedback in five-minute videos, whereas
text-based feedback on similar assignments, when spoken, was less than one minute long. The
analysis of Borup et al. (2015) found significant differences (p < .01) in the average word count of
video feedback compared to text feedback, with more the triple the number of words in a video on
one assignment and nearly double on two others. Elola & Oskoz’s (2016) analysis indicated that
instructors providing screencast feedback used more words, addressed more topics, and did so in
greater detail. Likewise, Thomas et al. (2017) reported an average count of 190 words for video-
based feedback compared with 103 words for text-based feedback. Five other studies provided
interview and focus-group data that supported the conclusion that video-based feedback was more
plentiful in terms of words provided (Hyde, 2013; Jones et al., 2012; Moore & Filling, 2012;
Orlando, 2016; West & Turner, 2016). In another study, instructors reported that speaking was
faster than writing and that they would not take the time to write out the comments they provided
in their videos (Vincelette & Bostic, 2013).
Research on the overall quality of video-based feedback was also positive. Some
instructors perceived video-based feedback to be more in-depth (Harper et al., 2015; Hyde, 2013).
The multimedia nature of screencast feedback yielded cognitive and meta-cognitive benefits
(Harper et al., 2015). Rather than expecting students to figure out typed comments or codes
independently, instructors were able to provide an in-depth recorded audio explanation synced to
on-screen visuals (Harper et al., 2015). The digital multimedia environment allowed an instructor
to include course documents and external resources in the screencast (Séror, 2012). This “show
and tell” approach resulted in the perception of fuller explanations of problems as well as possible
solutions (Séror, 2012).
In several studies, instructors perceived that the contents of video-based feedback
addressed higher-order thinking (Henderson & Phillips, 2015; Lamey, 2015; Orlando, 2016;
Vincelette & Bostic, 2013). Video-based feedback, for example, focused more on big-picture ideas
(Henderson & Phillips, 2015; Orlando, 2016; Vincelette & Bostic, 2013) as opposed to identifying
mechanical issues such as grammar and spelling (Lamey, 2015). Henderson & Phillips (2015)
reported on a blog entry by an instructor who wrote that providing video feedback felt more like
being a teacher than an editor, resulting in increased enjoyment of marking. Lamey (2015) noted
that recording video resulted in student-centred, substantive comments, making the experience
more like an in-person feedback conference. An instructor interviewed by Vincelette & Bostic
(2013) said the video feedback encouraged her to comment on higher-level concepts that she would
not usually take the time to type, resulting in more in-depth comments on the submission content.
One study, by contrast, reported video-based feedback contained more small talk than text-based
feedback (Thomas et al., 2017).

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Connecting with Students
Instructors felt video-based feedback was more personal than text-based feedback and
fostered a greater connection to students (Borup et al., 2014; Jones et al., 2012; Mathisen, 2012;
Orlando, 2016; Séror, 2012). When interviewed, instructors in the study of Mathisen (2012)
indicated that they became closer to students by providing screencast feedback, and the method
made them more personal and encouraging. Perhaps the sense of cohesion resulted from deliberate
strategies such as addressing students by name and acknowledging their personalities (Borup et
al., 2014). However, the audio and video of video feedback added a personal touch that amplified
the strategies, helping to form connections (Borup et al., 2014). Instructors perceived even
incidental visual details such as items on a bookshelf in the video background to help connect with
students (Borup et al., 2014). Instructors also thought that their ability to express emotion was
improved because students could hear the tone of their voice (Borup et al., 2014; Harper et al.,
2015; Parton et al., 2010; Séror, 2012). Séror (2012) commented that recorded feedback added
emotional colour, helping to convey authentic sincerity of praise and genuine confusion of
misunderstanding. Furthermore, instructors considered video-based feedback to be more
interactive and engaging than text-based feedback, thereby eliciting stronger feelings of
connection (Borup et al., 2014; Jones et al., 2012; Séror, 2012).
Sustainability of Video-Based Feedback
Several studies reported mixed evidence regarding the sustainability of producing video
feedback (Harper et al., 2015; Henderson & Phillips, 2015; Soden, 2016; West & Turner, 2016).
Henderson & Phillips (2015) thought the provision of video-based feedback was as fast and easy
as text-based feedback and could be a sustainable practice. However, they saved time by taking no
notes before recording. They also discouraged using screencast feedback because it might shift
focus to minute details of the student’s submission and become too time-consuming (Henderson
& Phillips, 2015). A focus group discussion of instructors indicated that providing video feedback
was positive and could be a sustainable practice. However, no follow-up was reported to indicate
the extent to which those instructors used video feedback after the study (West & Turner, 2016).
Soden (2017) reported that four out of six instructors tried using video-based feedback in the
previous two years but had not continued using it. Interview data indicated that a significant barrier
to the continued use of screencast feedback was failing to perceive its relative advantage (Soden,
2017).
On the other hand, in a study of nine foreign-language faculty members, a follow-up survey
indicated that three of five responding faculty continued to provide video-based feedback one year
after the intervention (Harper et al., 2015). Two instructors who did not continue were uncertain
about institutional policies about the method but remained optimistic about the approach and were
open to using it again (Harper et al., 2015). The instructors valued the advantage of video feedback
providing extended explanations (Harper et al., 2015).
SUMMARY AND PRACTICAL IMPLICATIONS
Four key issues surfaced in a systemic review of the literature on instructors’ perspectives
of using video-based feedback in higher education: creation, quality, connection, and

10 Bahula and Kay
sustainability. Many, but not all, studies suggested that video-based feedback was relatively easy
and time-efficient to create. However, several potential barriers to creating video-based feedback
were identified based on the recording environment, editing, performance, and approach to
presenting content. These barriers indicate that the design and creation process is detailed,
somewhat complicated, and needs to be supported with sufficient guidance and training to be
successful. In addition, the research also suggests that video feedback is superior in terms of both
quantity and quality. This type of feedback provides more detail, depth, and interactivity.
Numerous studies also reported that video-based feedback created stronger personal and cognitive
connections with students. It is somewhat puzzling, then, given the clear relative advantage of
video-based feedback (efficiency, quality, and connection), that more studies did not support the
sustainability of this approach. More research is needed to examine why instructors might choose
to discontinue providing video-based feedback.
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online courses. American Journal of Distance Education, 26(3), 143–156.
https://doi.org/10/gg57f7
Mathisen, P. (2012). Video feedback in higher education: A contribution to improving the quality
of written feedback. Nordic Journal of Digital Literacy, 7(2), 97–113.
https://www.idunn.no/dk/2012/02. https://doi.org/10/gm4vw7
Mayhew, E. (2017). Playback feedback: The impact of screen-captured video feedback on student
satisfaction, learning and attainment. European Political Science, 16(2), 179–192.
https://doi.org/10/gg57hk
McCarthy, J. (2015). Evaluating written, audio and video feedback in higher education summative
assessment tasks. Issues in Educational Research, 25(2), 153–169.
http://www.learntechlib.org/p/161352
Moore, N. S., & Filling, M. (2012). iFeedback: Using video technology for improving student
writing. Journal of College Literacy & Learning, 38, 3–14. https://j-cll.org/volume-38-
2012
O’Malley, P. J. (2011). Combining screencasting and a tablet PC to deliver personalised student
feedback. New Directions in the Teaching of Physical Sciences, 7, 27–30.
https://doi.org/10/gg57fx
Orlando, J. (2016). A comparison of text, voice, and screencasting feedback to online students.
American Journal of Distance Education, 30(3), 156–166. https://doi.org/10/gg57hn
Özkul, S., & Ortaçtepe, D. (2017). The use of video feedback in teaching process‐approach EFL
writing. TESOL Journal, 8(4), 862–877. https://doi.org/10/ghfcrq
Parkin, H. J., Hepplestone, S., Holden, G., Irwin, B., & Thorpe, L. (2012). A role for technology
in enhancing students’ engagement with feedback. Assessment & Evaluation in Higher
Education, 37(8), 963–973. https://doi.org/10/d8njhq

13 Bahula and Kay
Parton, B. S., Crain-Dorough, M., & Hancock, R. (2010). Using flip camcorders to create video
feedback: Is it realistic for professors and beneficial to students. International Journal of
Instructional Technology & Distance Learning, 7(1), 15–23.
http://www.itdl.org/Journal/Jan_10/article02.htm
Pitt, E., & Norton, L. (2017). ‘Now that’s the feedback I want!’ Students’ reactions to feedback on
graded work and what they do with it. Assessment & Evaluation in Higher Education,
42(4), 499–516. https://doi.org/10/gdqbvq
Price, M., Handley, K., Millar, J., & O’Donovan, B. (2010). Feedback: All that effort, but what is
the effect? Assessment & Evaluation in Higher Education, 35(3), 277–289.
https://doi.org/10/drrnc3
Richards, K., Bell, T., & Dwyer, A. (2017). Training sessional academic staff to provide quality
feedback on university students’ assessment: Lessons from a faculty of law learning and
teaching project. The Journal of Continuing Higher Education, 65(1), 25–34.
https://doi.org/10/gg57fr
Robinson, S., Centifanti, L., Brewer, G., & Holyoak, L. (2015). The benefits of delivering
formative feedback via video-casts. UCLan Journal of Pedagogic Research, 6(1).
http://pops.uclan.ac.uk/index.php/ujpr/article/view/326/0
Ryan, T., Henderson, M., & Phillips, M. (2019). Feedback modes matter: Comparing student
perceptions of digital and non‐digital feedback modes in higher education. British Journal
of Educational Technology, 50(3), 1507–1523. https://doi.org/10/gg57hg
Schilling, W., & Estell, J. K. (2014). Enhancing student comprehension with video grading.
Computers in Education Journal, 5(1), 28–39. http://asee-
coed.org/index.php/coed/article/view/Schilling_Enhancing
Semke, H. D. (1984). Effects of the red pen. Foreign Language Annals, 17(3), 195–202.
https://doi.org/10/fnqggc
Séror, J. (2012). Show me! Enhanced feedback through screencasting technology. TESL Canada
Journal, 30(1), 104–116. https://doi.org/10/gg57hs
Shields, S. (2015). ‘My work is bleeding’: Exploring students’ emotional responses to first-year
assignment feedback. Teaching in Higher Education, 20(6), 614–624.
https://doi.org/10/gf9k57
Silva, M. L. (2012). Camtasia in the classroom: Student attitudes and preferences for video
commentary or Microsoft Word comments during the revision process. Computers and
Composition, 29(1), 1–22. https://doi.org/10/gcpgt7
Soden, B. (2016). Combining screencast and written feedback to improve the assignment writing
of TESOL taught master’s students. The European Journal of Applied Linguistics and
TEFL, 5(1), 213–236.
http://www.theeuropeanjournal.eu/download/EJALTEFL_01_2016.pdf
Soden, B. (2017). The case of screencast feedback: Barriers to the use of learning technology.
Innovative Practice in Higher Education, 3(1), 1–21.
http://journals.staffs.ac.uk/index.php/ipihe/article/view/109
Sommers, J. (1989). The effects of tape-recorded commentary on student revision: A case study.
Journal of Teaching Writing, 8(2), 49–76.
https://journals.iupui.edu/index.php/teachingwriting/article/view/1012.
Sommers, J. (2013). Response 2.0: Commentary on student writing for the new millennium.
Journal of College Literacy & Learning, 39, 21–37. https://j-cll.org/volume-39-2013

14 Bahula and Kay
Thomas, R. A., West, R. E., & Borup, J. (2017). An analysis of instructor social presence in online
text and asynchronous video feedback comments. The Internet and Higher Education, 33,
61–73. https://doi.org/10/f96nbn
Thompson, R., & Lee, M. J. (2012). Talking with students through screencasting:
Experimentations with video feedback to improve student learning. The Journal of
Interactive Technology and Pedagogy, 1(1).
https://jitp.commons.gc.cuny.edu/2012/02/17/
Turner, W., & West, J. (2013). Assessment for “digital first language” speakers: Online video
assessment and feedback in higher education. International Journal of Teaching &
Learning in Higher Education, 25(3), 288–296.
http://www.isetl.org/ijtlhe/past2.cfm?v=25&i=3
Vincelette, E. J., & Bostic, T. (2013). Show and tell: Student and instructor perceptions of
screencast assessment. Assessing Writing, 18(4), 257–277. https://doi.org/10/gcpgkz
Weaver, M. R. (2006). Do students value feedback? Student perceptions of tutors’ written
responses. Assessment & Evaluation in Higher Education, 31(3), 379–394.
https://doi.org/10/cjknpn
West, J., & Turner, W. (2016). Enhancing the assessment experience: Improving student
perceptions, engagement and understanding using online video feedback. Innovations in
Education and Teaching International, 53(4), 400–410. https://doi.org/10/gg57h4

15 Bahula and Kay
Appendix A – Coding of Studies
Data item
Description
Values
Author(s)
The author(s) who wrote the article.
First author (and second, if there are only
two). See References for additional
authors.
Year
The year the article was published.
Year
Country
The country in which the study was conducted.
Three-letter country code
Population
The academic level of the population being
studied.
4, Undergraduate
5, Graduate
6, Pre-service teachers
Discipline
The academic discipline in which the study was
conducted.
1, Business
2, Education
3, Humanities
4, Language learning
5, Multidisciplinary
6, Social sciences
7, STEM
8, Other
Instructional mode
The instructional mode of the course in which the
feedback was provided.
1, Classroom
2, Blended
3, Asynchronous online
Assessment type
The purpose of the assessment (and of the
feedback provided).
1, Formative
2, Summative
Feedback recipient
The person(s) who the feedback was directed
toward. That is, how specific or generic was the
feedback.
1, Individual
2, Group
Feedback media 1, 2, 3
The feedback media that were explicitly studied.
Feedback media 1 is the data item for the first
condition. Feedback media 2 and 3 are the data
items for any comparison conditions.
0, Face-to-face
1, Video
2, Screencast
3, Pencast
4, VoiceThread
5, Audio
6, Digital markup
7, Handwritten markup
8, Rubric
Feedback length
The length in minutes of the video-based feedback
provided.
Number of minutes
Capture method
The method used to capture the feedback.
1, Adobe Connect
2, CamStudio
3, Camtasia
4, Camtasia Relay
5, Camtasia Studio
6, Canvas
7, Flip
8, iMovie
9, iPhone
10, Jing
11, Logitech
12, Multiple
13, Photo Booth
14, QuickTime Player
15, Screencast-O-Matic
16, SnagIt

16 Bahula and Kay
Data item
Description
Values
17, VoiceThread
18, Windows Media Encoder
Research method
The research method(s) used in the study.
1, Qualitative
2, Observation
3, Questionnaire
Reliab
Whether the reliability of quantitative statistics in
the study is discussed.
0, No
1, Yes
Valid
Whether the validity of quantitative statistics in the
study is discussed.
0, No
1, Yes
Qual
What level of checks were used on qualitative
data.
0, None
1, One
2, Multiple
Student sample
The number of students who participated in the
study by completing a survey, focus group,
interview, etc.
Number of students
Instructor sample
The number of instructors who participated in the
study by completing a survey, focus group,
interview, self-reflection, etc.
Number of instructors
Sample desc
The completeness of the sample description.
0, No description
1, Partial description
2, Complete description

Appendix B – Table of Coded Articles, Part 1
Author(s)
Year
Country
Population
Discipline
Instructional
Mode
Assessment
Type
Feedback
Recipient
Feedback
Media 1
Feedback
Media 2
Feedback
Media 3
Feedback
Length
Capture
method
Anson
(2015)
USA
4
6
1
1 (?)
1
2
6
4
10
Armağan et
al.
(2016)
TUR
4
4
1
1
1
2
Bissell
(2017)
GBR
4
3
1
2
1
2
10
15
Borup et al.
(2014)
USA
6
2
2
1
1
1
6
6
Borup et al.
(2015)
USA
6
2
2
1 + 2
1
1
6
6
Crook et al.
(2012)
GBR
4 + 5
5
1
1
2
2
1
3
2 + 8
Cunningham
(2019)
USA
4
4
1
1
1
2
6
4-11
16
Denton
(2014)
USA
4
2
1 (?)
1
1
2 + 8
3.17
Edwards et
al.
(2012)
GBR
5
1
3
2
1
2 + 8
6 + 8
5
10
Elola and
Oskoz
(2016)
USA
4
4
1
1
1
2
6
15
15
Gonzalez
and Moore
(2018)
USA
5
2
2
1
1
4
17
Griffiths and
Graham
(2009)
USA
6
2
3
1
1
Griffiths and
Graham
(2010)
USA
6
2
3
1
1
1
12
Hall et al.
(2016)
USA
4
3
1
1
1
1
7
5
13
Harper et al.
(2015)
GBR
4
4
3 (?)
2 (?)
1
2 + 6
6
5
10
Henderson
and Phillips
(2015)
AUS
4 + 5
2
2
2
1
1
5
9 + 11
Hope
(2011)
GBR
4
7
1 (?)
2
1
2
6
5-13
10
Hyde
(2013)
GBR
4
8
1
2
1
2
5
10
Jones et al.
(2012)
GBR
4 + 5
1
1
1 (?)
1
2
7
6
18
Lamey
(2015)
USA
4
3
1 (?)
2 (?)
1
1
7
4
13
Mathieson
(2012)
USA
5
8
3
2
1
2 + 6
6
5
10
Mathisen
(2012)
NOR
4
5
1 (?)
1 + 2
1 + 2
2 + 6
5
10
Mayhew
(2017)
GBR
4
6
1
2
1
1 + 2
4-10
5
McCarthy
(2015)
AUS
4
3
1
2
1
2
5
6 + 8
4
5
Moore and
Filling
(2012)
USA
4
3
1
1 + 2
1
1 OR 2
6 (?) + 8
5-15
8 + 14
O’Malley
(2011)
GBR
5
7
1 (?)
1
1
3
5-10
5

18 Bahula & Kay
Author(s)
Year
Country
Population
Discipline
Instructional
Mode
Assessment
Type
Feedback
Recipient
Feedback
Media 1
Feedback
Media 2
Feedback
Media 3
Feedback
Length
Capture
method
Orlando
(2016)
USA
5
3 (?)
1
2
5
6
15
Parton et al.
(2010)
USA
5
2
2
1 + 2
1
1
1 + 7
7
5
7
Robinson et
al.
(2015)
GBR
4
6
1
1
1
2 + 6
6
0
10-20
1
Schilling and
Estell
(2014)
USA
4
7
2
2 (?)
1 + 2
2 + 8
3-22
Séror
(2012)
CAN
4
4
1 (?)
1
1
2 + 8
5
10
Silva
(2012)
USA
4
7
2
1
1
2
6
7-14.5
3
Soden
(2016)
GBR
5
2
1
1
1
2 + 6
6
5-6
15
Soden
(2017)
GBR
4 + 5
5
1 (?)
1
1
2
2-20
12
Sommers
(2013)
USA
4
3
1 (?)
1
2
5
10
Thomas et
al.
(2017)
USA
6
2
2
2
1
1
6
Turner and
West
(2013)
AUS
6
2
1 (?)
1
2
6-12
4
Vincelette
and Bostic
(2013)
USA
4
3
1
2 (?)
1
2
5
10
West and
Turner
(2016)
AUS
6
2
1
1
2 + 8
10-20
4

19 Bahula & Kay
Appendix C – Table of Coded Articles, Part 2
Author(s)
Year
Research
Method
Reliability
Valid
Qual
Student
Sample
Instructor
Sample
Sample
Desc
Anson
(2015)
3
0
0
0
95
3 (?)
0
Armağan et al.
(2016)
1
0
40
3
1
Bissell
(2017)
3
0
0
0
15
1
0
Borup et al.
(2014)
1 + 3
1
1
2
130
10
2
Borup et al.
(2015)
1 + 2 + 3
0
1
1
229
9
1
Crook et al.
(2012)
3
0
0
0
314
27
2
Cunningham
(2019)
1 + 2 + 3
0
0
0
12
1
1
Denton
(2014)
2 + 3
1
1
36
1
1
Edwards et al.
(2012)
3
1
0
0
14
1
0
Elola and Oskoz
(2016)
2 + 3
1
0
0
4
1
1
Gonzalez and
Moore
(2018)
1
0
18
2
0
Griffiths and
Graham
(2009)
3
0
0
0
38
1
1
Griffiths and
Graham
(2010)
2
0
0
1
3
1
Hall et al.
(2016)
3
0
0
1
40
3
1
Harper et al.
(2015)
1 + 2 + 3
0
0
1
54
9
0
Henderson and
Phillips
(2015)
1 + 3
0
126
2
1
Hope
(2011)
2 + 3
0
0
0
145
1
1
Hyde
(2013)
1
0
10
1
0
Jones et al.
(2012)
1 + 3
1
1
0
75
20
1
Lamey
(2015)
1 + 3
0
0
0
74
1
0
Mathieson
(2012)
3
0
1
0
13
1
1
Mathisen
(2012)
1 + 3
0
1
0
120
6
1
Mayhew
(2017)
1 + 2 + 3
0
0
0
50
1
1
McCarthy
(2015)
1 + 3
0
0
0
77
1
2
Moore and Filling
(2012)
1 + 2 + 3
0
0
1
45
2
2
O’Malley
(2011)
3
0
0
0
1
0
Orlando
(2016)
3
0
0
0
30
6
0
Parton et al.
(2010)
1 + 3
0
0
0
12
1
0
Robinson et al.
(2015)
3
0
0
0
18
2
0

20 Bahula & Kay
Author(s)
Year
Research
Method
Reliability
Valid
Qual
Student
Sample
Instructor
Sample
Sample
Desc
Schilling and Estell
(2014)
3
0
0
0
70
2
0
Séror
(2012)
1
0
0
0
1
0
Silva
(2012)
2 + 3
0
0
0
19
1
2
Soden
(2016)
1 + 2
0
9
1
1
Soden
(2017)
1
0
6
2
Sommers
(2013)
2 + 3
0
0
0
97
1
0
Thomas et al.
(2017)
2
1
0
1
167
6
1
Turner and West
(2013)
3
0
0
0
59
2
1
Vincelette and
Bostic
(2013)
1 + 3
1
1
1
39
5
2
West and Turner
(2016)
3
0
0
1
299
8
1