As some of you know, I’m writing a book on teacherbots, chatbots and artificial intelligence in education. Which means that I have conversations with chatbots every now and then. Like this one:
“Precision education” has attracted attention as an instantiation of personalized learning, but my interest in it lies in its (a) perspective of flexibility, and (b) it’s adoption of medical thinking/terminology vis-a-vis education research that critiques such models.
The purchase is part of National University’s push to try new forms of online education. Last year it announced a new higher-education model it calls “precision education,” though officials say that this project was separate. Both projects involve breaking the college curriculum down into small modules and letting students complete them at their own pace.
[…]Students pay the flat $8,500 a year for tuition, but they can work through the material at their own pace. “Some people will take four years to get a four-year degree, some people will take three years, some people will take five years,” Cunningham said. “Our goal is to build a library of digital content broken down into its smallest possible components.”
The idea is to replace one-size-fits-all lectures with a range of mix-and-match educational materials, so delivering a course can be done the way a doctor might consider different treatment options for different patients.
Officials are calling the approach “precision education,” a nod to the practice of “precision medicine” that is gaining popularity for cancer treatments and other ailments. And today National University, which specializes in serving nontraditional students, announced that it will spend $20 million over the next three years converting dozens of general-education courses to a precision model.
The 2018 Artificial Intelligence in Education conference starts today. Its full proceedings are freely available online until July 21st, and I scrolled through them to identify papers/posters/reports that seemed potentially relevant to my work. These are of interest to me because some use methods that seem worthwhile, others offer insightful results, and yet others seem to make unsubstantiated claims. As an aside, I especially like the fact that AIED offers space for PhD students to discuss their proposed research.
Here’s the papers that I identified to read:
Leveraging Educational Technology to Improve the Quality of Civil Discourse
Towards Combined Network and Text Analytics of Student Discourse in Online Discussions
An Instructional Factors Analysis of an Online Logical Fallacy Tutoring System
Adapting Learning Activities Selection in an Intelligent Tutoring System to Affect
Preliminary Evaluations of a Dialogue-Based Digital Tutor
ITADS: A Real-World Intelligent Tutor to Train Troubleshooting Skills
Early Identification of At-Risk Students Using Iterative Logistic Regression
Smart Learning Partner: An Interactive Robot for Education
A Data-Driven Method for Helping Teachers Improve Feedback in Computer Programming Automated Tutors
The top definition of YouTube comments in the urban dictionary is the following: “the only place where a polite discussion about kittens can lead to a flame war about government conspiracies.”
Inquisitive readers might ask: Is that flame war the same for all videos? Or is it more likely for some videos than others?
Our latest paper (and when I write our, I am referring to Royce k=Kimmons, Tonia Dousay, Patrick Lowenthal, and Ross Larsen) explores whether the sentiment expressed toward scholars who go online varies according to variables of interest. Put differently, scholars are encouraged to be present online, to establish a digital identity, and expand their reach and impact. But, what is the public’s reaction? Does the public react more positively/negatively to some people? There’s many ways to go about exploring this question. We sought to answer this question by examining YouTube comments, but one could investigate tweets, blog comments, self-reported data, and so on. Below is our abstract, summarizing our findings, and link to our paper. Note the impact of gender, animations, and moderation on expressed sentiment:
Veletsianos, G., Kimmons, R., Larsen, R., Dousay, T., & Lowenthal, P. (2018). Public Comment Sentiment on Educational Videos: Understanding the Effects of Presenter Gender, Video Format, Threading, and Moderation on YouTube TED Talk Comments. PLOS ONE 13(6): e0197331. https://doi.org/10.1371/journal.pone.0197331
Scholars, educators, and students are increasingly encouraged to participate in online spaces. While the current literature highlights the potential positive outcomes of such participation, little research exists on the sentiment that these individuals may face online and on the factors that may lead some people to face different types of sentiment than others. To investigate these issues, we examined the strength of positive and negative sentiment expressed in response to TEDx and TED-Ed talks posted on YouTube (n = 655), the effect of several variables on comment and reply sentiment (n = 774,939), and the projected effects that sentiment-based moderation would have had on posted content. We found that most comments and replies were neutral in nature and some topics were more likely than others to elicit positive or negative sentiment. Videos of male presenters showed greater neutrality, while videos of female presenters saw significantly greater positive and negative polarity in replies. Animations neutralized both the negativity and positivity of replies at a very high rate. Gender and video format influenced the sentiment of replies and not just the initial comments that were directed toward the video. Finally, we found that using sentiment as a way to moderate offensive content would have a significant effect on non-offensive content. These findings have far-reaching implications for social media platforms and for those who encourage or prepare students and scholars to participate online.
What advice and resources would you offer to early-career instructional designers? That’s the question Inside Digital Learning asked practicing instructional designers. There’s many worthwhile insights for aspiring designers in the piece. I enjoyed reading it, and you might like it, too.
I’d like to add three points:
– The Association for Educational Communications and Technology (AECT) is the flagship organization for instructional designers in North America. The organization’s annual convention is usually in October. Members participate in one or more divisions of interest (e.g., Distance Learning, School Media & Technology, etc), and divisions often offer free webinars. For example, here’s Dr. Patrick Lowenthal discussing the use of live meetings in asynchronous online courses. [Disclosure: I have been a member of AECT since 2005, and at one point I was the president of the Research & Theory division. In other words, I’m biased]. Other organizations that may be of interest to IDs, but don’t necessarily focus on ID as much as Educause, OLC, and ALT (UK).
– Instructional design is about problem-solving. Are you interested in helping others solve instructional and learning problems? You’re in the right field. You might be asked to collaborate with others in order to improve learning outcomes, reduce dropout rates, improve course participation rates, convert face-to-face courses to online courses, and so on. Be warned though: Some problems may be more interesting than others, and even though instructional designers should be working collaboratively with faculty members and others(e.g., media developers, data scientists, etc), that doesn’t always happen (unfortunately).
– Problem-solving is not just about technology. The best way to illustrate this is to use a problem from the list above, so let’s pick the wicked problem of “improving learning outcomes.” Novice instructional designers might gravitate towards exploring what technologies might help them address the problem, in what Tanya Joosten describes as the act of throwing spaghetti on the wall hoping that it will stick. Instructional design involves analysis: Why are learning outcomes poor? Might it be that they are not well-defined? Are objectives, instruction, and assessment well-aligned? Perhaps enrolled learners don’t have the pre-requisite knowledge coming into the course? Could it be that learners are facing significant challenges that have nothing to do with the content of the course, but which nonetheless conflict with the design of the course? Solutions to these (and a slew of other problems) can be found in redesigning courses, policies, and practices without necessarily adding/removing technology to/from the mix. In our Master’s program, we highlight design – sometimes coupled with technology, often without – as central to innovation.
What other advice do you have for aspiring instructional designers?
Two colleagues and I just finished an external evaluation of a universityʼs online MA in education and MEd programs. The programs are stellar, the students are engaged, and the faculty are thoughtful. Their graduation rates are above 90% and their students do important work, evidenced in part by the number of theses that are subsequently published and the number of projects that seek to make meaningful contributions to practice. The programs do many things right.
You have to look inside to get a clear view of what is happening
The photo is of Georgetown University, and has no relationship to the program evaluated
Their outcomes contradict the opinion that online learning is solitary and lacks inclusion. Rather – and despite the fact that these programs are thriving – they face institutional obstacles that prevent them from doing better, that preclude them from further expanding equity and quality. We have a few recommendations for improvement, including suggestions for course design, evaluation, assessment, and enrolment, and Iʼm looking forward to following their work in the future. Being able to examine degree programs in depth and interview faculty, staff, administrators, and students is a worthwhile experience in its own right.
This program is a single case, and by no means an accurate reflection of online programs in general. However, the more I do these evaluations the more I see online learning curtailed not just by forces external to the institution, but also by recurring internal barriers that staff, faculty, and administrators can address.
Responses to the news relating to the Cambridge Analytica + Facebook scandal have been swift with many vouching to #DeleteFacebook. An extensive collection of resources relating to this scandal are here. Lee Skallerup Bessette calls the fiasco the latest iteration of “guess how safe and secure your data is and how it might be used for nefarious purposes but it’s actually worse than that.”
An Angus Reid poll in Canada shows that 3 out of 4 respondents indicated that they plan on changing the ways they use the platform. How many will actually change their practices and behavior? Rehardless, I wonder what people do when their habits center around mistrusting contemporary digital platforms and their opaque use of our data. In other words, what do you do on an ongoing basis when you anticipate that benevolence isn’t the distinguishing characteristic of social media platforms?
For example, like others, I:
- purge my historical tweets (because bad actors can easily take them out of context)
- use authenticator apps
- use browser extensions that block ads and trackers
- delete unused online accounts and profiles (unless of course you really still need your ICQ account)
- rarely connect distinct apps (e.g., google with dropbox)
I’m quite sure I take a number of other, likely unconscious, steps that I’ve picked up over time for privacy’s sake. For instance, after thinking about this for a couple of minutes I remembered that I installed an app on my website that aims to limit brute-force login attempts. And it strikes me that many of the conscious (and unconscious) steps that I take are rarely enabled by the platforms that are thirsty for data: There’s no bulk delete button on Twittter; there’s no “unfollow all the pages I currently follow” on Facebook; and so on.
What steps do you take to minimize the likelihood of your social media data being used in unanticipated ways?