This semester I am aiming to take a tentative step into AI for teaching. This will be in three workshops planned at ANU, for the computer project student's capstone e-portfolio for the ANU Techlauncher program. The students have to prepare a portfolio in the form of an application for a real job. The question was: do we try to ban students from using generative AI to help them with this, or do we show them how to use it effectively and ethically? I will attempt to do the latter.
The ANU now provides Microsoft Copilot, as part of the Office suite. This provides the opportunity to take studnts through exercises to use it, to help with their writing. I have not used Copilot, but have explored the technology it is based on since 2022. The idea is to get the students in workshop groups to ask a career related question of Copilot, then critique & improve the answer.
A couple of weeks ago I attended a two-day Symposium at USyd on using AI this way, with team-based learning. One tip given at the symposium to stop students simply relying on the answers given by the AI. The idea is to prompt students with a very localized question, which the AI model can only answer with generalities:
Last week I attended an ANU AI Assessment Question Drop In Session. The impression I got was that the ANU would not be averse to using AI this way. One of the other people who dropped in for advice was already proposing to run some team-based AI sessions along these lines.
On a visit to Singapore for EduTech Asia 2022, I will stopping off at a university to talk about using a job application as the capstone for work integrated learning. I would be happy to talk at other institutions, while in Singapore, or elsewhere. This is an update to a paper I gave at the 2019 IEEE International Conference on Engineering, Technology and Education (TALE). That was a few months before the COVID-19 pandemic, and I had no idea that my idea of designing a blended learning module which could be quickly converted for full online delivery in an emergency would be needed so soon. I will discuss how it went and what universities should do next post-pandemic.
"Learning to Reflect" is a module for the ANU TechLauncher program, where students reflect on what they have learned, by writing an application for a real job, as their last assessed task before graduating. This was developed in late 2018 and first run in semester 1, from February 2019. This was designed for blended delivery, with theoption of easy conversion to full online delivery. That option was needed for Semesters 1 and 2 in 2020 due to COVID-19. The 2021 version is intended to be run online, with a blended delivery option ready for a return to the classroom and is available under a CC-BY licence.
Two small workshop exercises have been added for a 2% mark each, in place of the quizzes and forums used previously. The first assignment has been dropped, to make the assessment less complex. An optional student logbook has been added, to aid student reflection and deter plagiarism.
A paper on the design and blended delivery of the module is available:
"Learning to Reflect" is a module for the ANU TechLauncher program developed in late 2018 and first run in semester 1, from February 2019. This was designed for blended delivery, with the option of easy conversion to full online delivery. That option was needed for Semester 1, February 2020 due to COVID-19,. The two face to face workshops were replaced with Zoom video conferences. The online content and activities were unchanged.
The assessment was removed from two online quizzes and forums, leaving just two assignments to provide 20% of Techlauncher assessment. This was done in an attempt to reduce the burden on students, and staff, who were suddenly required to change from face-to-face to online learning. The students still had their tutors at Zoom video-conferences, to help them keep up with their studies.
About 90% of students still completed the quizzes, despite them not counting towards assessment. In contrast, about 90% of students did not post regularly to the online forums. The difference may be because the quizzes were quick and easy to complete, being multiple choice, whereas the forum posts required composing text. Also the quizzes returned instant feedback in the form of connect/incorrect and a numeric score, whereas there was no automated feedback in the forums. When the forums are assessed, there is feedback from students (which is also assessed). However, some form of tutor-bot might be useful to give instant feedback.
A paper on the design and blended delivery of the module is available:
There is a risk with online leaning that the student will become overwhelmed with information and so miss critical messages from their instructor. Aircraft pilots experience a similar problem, overloaded with information from the panels of instruments around them. The Dark Cockpit philosophy was developed to address this:
“... dark cockpit philosophy which
minimizes distracting annunciation for pilots, i.e. only abnormal or
transition states are visible. So, the normal parameters of the engine during flight do not light up any interface lights.” (Jambon, Girard &
Aït-Ameur, p.5, 2001).
Applying the Dark Cockpit philosophy to e-learning, the student should only get a message or other indicator, when there is something they need to do, or to confirm something they did (such as submit an assessment task).
In the notes for instructors in my Learning to Reflect module, I suggest the Instructor seeds the online discussion
forums with questions and then leave the students to discuss it. Too many times I see instructors stifling student discussion by continually interrupting, correcting students. It seems at times this is due to insecurity: the instructor wants to show they know more that the student. Also it may be due to a lack of training in how to teach: the only way the tutor know to teach is by telling the student.
In contrast, I suggest the instructor issue “nudges” occasionally to the
groups, or individual students, where there appears to be a problem.
Does anyone else do this?
References
Jambon, F., Girard, P., &
Aït-Ameur, Y. (2001, May). Interactive System Safety and Usability
enforced with the development process. In IFIP International
Conference on Engineering for Human-Computer Interaction (pp.
39-55). Springer, Berlin, Heidelberg. URL
https://link.springer.com/content/pdf/10.1007%2F3-540-45348-2_8.pdf#page=5
I have updated my "Learning to Reflect" learning module for the ANU TechLauncher program this semester. The students are guided in how to prepare a job application. In the process they learn long term professional skills to identify their development needs, how they will
acquire these and to reflect on what they have learned. The materials are available under a creative commons license for modification and reuse.
Introduction
The
notes and videos contain content intended for instructors, as well as students, to
be used in conjunction with online exercises, and face-to-face
workshops. Students will be prompted by the Moodle Learning
Management System, as to which parts to read, and when to read them.
1. Learn
In this first of two parts, you will investigate what you need to learn
for your project, and long term for your career. In scope here, are both
technical skills and also professional and teamwork skills. The aim is
to prepare you to be a professional in your field, which includes the
ability to take charge and responsibility for your future professional
development.
2. Report and reflect
In this second and last part, you
will reflect on what you have learned. The assignment task is to select
a real position to prepare an application cover letter for, and revise the responses to selection criteria prepared in assignment 1.
Having revised the "Learning to Reflect" module notes for ANU TechLauncher students, it was time to revise the video. This is a time-consuming and exacting process, even for someone with training in video production.
Last semester I produced this by first making a slideshow presentation, which was also used live in the classroom. I prepared a script based on what was in the module notes, rearranged to match the sequence in the video. I then turned each slide into an image, and the script into synthetic speech. The slides and audio were then imported into a video editing package and timed to match the audio. The results were not perfect, but like Samuel Johnson's piano playing dog: "It's not done well; but you are surprised to find it done at all." ;-)
This time I decided to try Content Samurai, after it was demonstrated at PitchEd NSW. This is a web based tool which takes a script, and searches for suitable video clips, or still images, based on the key phrases. The service will then turn the script into synthetic speech timed to the video. This process works remarkably well. The Australian accented male voice provided is much better than those I have used previously. I did have to slow down the narration to 80% of full speed, and lower the volume of the background music to 5%.
After some experimentation I found I could set the system to add a new scene for each paragraph in the script. I could then hide the text, otherwise it would put it as a caption on screen. Also I could upload my slides in place of some of the auto suggested clips. This way my slides are interspersed with the suggested filler scenes.
Here is what the video looks like generated by automatically, with the defaults (video selected from keywords, scene
transitions, animated text). This version was not usable:
I then changed the settings, to use only still images, and have one per paragraph, with no animation, or scene transitions:
One problem was that rendering was very slow (but video rendering is always slow). I found it could be faster by using still images, rather than video clips. The rendering took about six minutes for a six minute video. Also I created a plain black template for the slides, with no shaded pattern.
One option I would like is to reduce the bit-rate of the audio. For an educational video you need only low quality mono sound.
One tip is to set your slide maker (I use LibreOffice) for 16:9 format slides, to match a modern widescreen TV. Then generate the slides images at the resolution required. Full HD TV is 1,920x1,080. Content Samurai produced the lower resolution
1,280x720HD TV format typically used for broadcast TV, but down sampled my higher resolution slides very cleanly.
I could not find a way to prepare closed captions within Content Samurai, so I uploaded the video to YouTube, and then downloaded the VTT file it produced. I then uploaded this to the Moodle website, for the students.
