Video Journal of Education and Pedagogy - catch up on articles

 I blogged about the Video Journal of Education and Pedagogy in 2016, when its first issue was published. Since then, the journal has shifted from Springer to Brill as the publisher. The editor is E.J. White from the University of Canterbury. 

The main focus of the video, is the application of video to the study of education and pedagogy. So far, in the 5 issues (only 1 issue of about 10 articles a year), the articles have been from the compulsory school and higher education sectors. So, a possibility to submit a vocational education context article. A few of the articles have video embedded, either from the research data or a short presentation of the article itself. I will continue to follow the journal as the inclusion of a video into a research article, is still relatively uncommon. I have not found another journal with similar approaches in the education field. 

coaches' eye - use in improving vocational education

We have been using coaches eye successfully (the Ipad App) for several years with hospitality students. Mainly for the improvement of skills and dispositional learning as students learn how to check in and check out guests.

This semester, we are trying coaches' eye app on surface pro tablets, with nursing students and office systems students. Nursing students to learn how to communicate with patients and other health staff using 'health' discipline protocols and office systems students learning how to chair meetings.

We have found coaches' eye to be better for skills learning rather than to improve communication skills requiring ongoing conversation.

Coaches' eye works best when there is not too much talking, and feedback is mainly on 'action'. In hospitality, the check-in / check-out process includes 'in-built' pauses in the conversational flow between receptionist and guests, allowing for aural feedback to be provided between students' activity. However, when there is a constant stream of conversation, it is much more difficult to provide oral feedback, as the oral feedback voices over the conversation between student and role play conversationalist. The pause button can be used to stop the video and offer feedback. However, there is a break in continuity in the learners' conversation and this lowers impact. Additionally, the role plays in nursing and office systems are quite long. Again, it becomes difficult to sustain attention when there are too many pauses in the feedback on a role play.

We are now working on focusing on key aspects of the role play. i e. what is the skill being practiced for each role play. Tutors then hone in on the key aspects and offer feedback only on key aspects. Meanwhile, I return to evaluating other possible video annotation tools, starting by a refreshment of work from several years back from two blogs in 2009, one on video analysis vs video annotation, the other on video analysis for multimodal discourse analysis.

Alternatives to Coaches Eye - video analysis app

We have been using Coaches’ Eye for a range of ‘skills improvement using videos’ projects. However, coaches’ eye apps struggles to run on the surface RT. Video annotation and playback is 'jerky' due to the surface RT's low spec processor.

Appcrawler offers a range of alternatives to coaches' eye. A comparative analysis of 3 apps - ubersense (ios and android), coaches' eye and dartfish - reveals coaches' eye as the only app available on Windows devices. There are more alternatives with regards to PC apps.Some of the examples looked at in previous post could be used, but apps rather than full blown PC software would be less memory hungry. MotionPro is a newer example, but at $100 plus, a bit expensive and we will also need Surface Pros to run the app.

So will still need to keep an eye for a suitable alternative. 

Learning practical skills via elearning

The earthquake of 22/2 has challenged us to think more deeply about how to deliver learning opportunities to students without access to practical workshops / teaching areas. A large amount of contact time is put into providing student with opportunities to practice new skills and to assist students with applying newly learnt knowledge to practical tasks and real-world scenarios.  Many of the CPIT programmes located in the now non-accessible city campus will have to re-develop how to helps students complete practical skills training without access to specialised training facilities and equipment.

I have been thinking the above over the weekend and  making connections to my learning of tai chi. I attend a weekly tai chi practice session and have been doing this for many years. Over the last few summers, our group have been attempting to learn a more advanced form of tai chi using a sword - called a jian. Our difficulty has stemmed from having teachers who are expert practitioners from China, who are usually on holiday in Christchurch, visiting family. Each of our teachers over the last few summers have been from different parts of China. So apart from contending with understanding regional dialects of Mandarin, each teacher practiced a different form of sword tai chi. So progress, has been slow and confusion reigns for about 3 or 4 sessions before we adapt, after which, the teacher returns to China, leaving us to unravel the form through notes and videos (if available).

My proposal was to video ourselves doing the form and sending the video to our teacher in China. However, this plan never took off due to mixed digital literacy of our Chinese teachers and camera shyness on the part of some of the tai chi learners.

I now plan to take the above concept further here at work. Students will still need to be provided with a hands-on learning opportunity whereever possible. Demonstrations and initial practice can then be supported by students doing practice at off-campus facilities (in workplace, possibly at home etc.) Practice can be videoed and emailed to tutors for formative assessment. After several iteractions, formative assessments can also be obtained by students sharing their videos with each other. Student will need to be provided with support sessions on providing peer-feed back. Hopefully, the above will replicate some of the contact time that will need to be replaced with self-directed activities. Using the above will still ensure students are provided with opportunities for feedback, even when they are working independently and physically distant from tutors and class mates. Will have a chat with our elearning support team about ramping up Mahara for the sharing process as it will be an ideal tool to use.

The above may be supplemented in some specialised areas by the use of video analysis software, for instance silicon coach, which is easier to use than other specialised research analysis software. Discreet skills in programmes like circoarts (juggling, riding unibike etc.) may be improved using comparative software and especially where key kineasthetic motor skills need to be honed before students are able to move on to more complex and integrated skill sets. If ideas for using video eventuate, it will be good to evaluate the process using a formalised research / evaluative study. Will progress this further this week to see how well the idea aligns with current approaches to teaching practical skills.

Comparing nVivo and alasti for video analysis

At present, the qualitative software analysis platform we use at CPIT is nVivo. I am now using nVivo with the ‘perspectives of first year apprentices’ project and the thematic analysis of interview data for the ‘perspectives of new trades tutors’ was completed using nVivo. I have discussed pros and cons of using Vivo before, Presently, I find it is easy to use and generally intuitive with regards to dealing with text analysis although I also take into account that nVivo does impose a certain way of thinking about qualitative analysis.


Discussions with a few other researchers who work with video data reveal several use atlasti. So I downloaded a free trial version of atlasti and have completed transcription of a short video clip with both sets of software. The free version of atlasti allows users to upload 10 primary documents, work with 50 codes, 100 quotations and 30 memos.

Here is a comparison of older versions of nVivo and Atlasti, As a comparison, of my own evaluation of the two, I have jotted down some notes.

The first task is to learn a whole set of new terms!! Atlasti – hermeneutic unit vs project on nvivo. Alasti quotes or quotations vs nodes in nVivo for themes. Networks in alasti vs models in nvivo etc etc.

Atlasti is more ‘windows like’ in layout but nVivo uses the concept of folders to store and navigate through the various layers of data. Atlasti is more intuitive to use for uploading ‘primary documents’ which are referred to as ‘sources’ in nVivo.

Importing videos into each programme meant we had to convert videos to the correct file format which could be read by each data analysis software package. We used any video converter which is a free download and easy to use. Converting between video formats using this tool is straightforward.

Time stamp on nVivo only allows for a minute intervals which are, at the moment, not fine enough with the transcripts we have been producing. Also only one column for timespan and another for comments. However, custom columns can be added.

Had to convert word table of transcripts into a rtf or txt file to upload as a ‘primary document’ in atlasti which mucked up the organisation of the word table. Assigning quotations (nodes in nVivo) was simple, similar to adding comments to a word document. Coding also straight forward using a drag and drop technique. Video coding using an editing technique to snip segments out of the video to code.

Coding video using nVivo also involves a snipping / selection process and then a drag and drop of the selected segment to the code required. The coding summary records the timespan or the transcript fragment.

On alasti, the coding is marked on the transcript on a side screen and the actual video clip comes up.

Will need to work with both nVivo and Atlasti for another couple of clips to become proficient at the technicalities of working with each tool. At the moment, they both complete similar tasks although for the moment, Atlasti provides a better method to access video segments which have been coded.

Video analysis software for multimodal data analysis

While browsing through literature on multi-modal analysis, I came across studiocode as a video data analysis software tool. I then asked around CPIT and found out the sports science people were using a NZ developed product called silicon coach which is developed specifically to analyse sports performances but also provides capabilities for comparison of individual performances and to build up resources based on annotations made on videos. Many video analysis software has a sports orientated slant as this is where it is most useful for analysing the performance of athletes.

I then did the usual google search and came up with a short article which recommended four video data analysis tools including studiocode (Mac OS only). The other three are annotation which is only Mac based but seemed to have an attractive, user friendly interface for US$299; ecove with a byline of software for gathering data while observing behaviour, runs also on Palm OS & Pocket PCs for US$189; & Observer xt which has both Mac & Windows versions plus a version for mobile devices, seems to have all the bells & whistles (like studiocode).

A comprehensive list of qualitative data analysis software provided over thirty examples with about a third capable of video and audio analysis. These include:-

Transana which is developed by the University of Wisconsin and open source and cost US$50 for single user and US$500 per project.

Dart fish which provides a free download trial for 30 days

Atlasti which is a standard qualitative data analysis provides for multimedia coding and supposed to be similar to nVivo.

Hyperresearch $399 as another alternative to the more expensive sports based video analysis tools.

A couple which are freeware to have a look at include Elan and signstream.

So plenty of choice for the moment, the most likely ones will need to be evaluated against nVivo. I am looking for one which will be easy to use as one of the goals of implementing multi-modal analysis protocols for observing teaching & learning at CPIT is to devolve the analysis to tutors.