George Veletsianos, PhD

Bots, AI, & Education update #3

By George Veletsianos

On March 23, 2018

In teacherbots

Today’s rough set of notes that focus on teacherbots and artificial intelligence in education

Chatbots: One of the technologies that’s mesmerized silicon valley
Humans have long promised future lives enhanced by machines
Many proponents highlight the qualities of bots vis-a-vis teachers
- personal
- personalized
- monitoring & nudging
- can give reliable feedback
- don’t get tired
- etc etc
Knewton: Algos to complement and support teacher (sidenote: as if anyone will be forthright about aiming to replace teachers… except perhaps this book that playfully states that “coaches (once called teachers)” will cooperate with AI)
Genetics with Jean: bots with affect-sensing functionality, ie software that detects students’ affective states and responds accordingly
Driveleress Ed-Tech: Robots aren’t going to march in for jobs; it’s the corporations and the systems that support them that enable that to happen.

Bots, AI, & Education update #2

By George Veletsianos

On March 23, 2018

In teacherbots

Yesterday’s rough set of notes that focus on teacherbots and artificial intelligence in education

Notable critiques of Big Data, data analytics, and algorithmic culture (e.g., boyd & Crawford, 2012; Tufecki, 2014 & recent critiques of YouTube’s recommendation algorithm as well as Caulfield’s demonstration of polarization on Pinterest). These rarely show up in discussions around bots and AI in education, critiques of learning analytics and big data (e.g., Selwyn 2014; Williamson, 2015) are generally applicable to the technologies that enable bots to do what they do (e.g., Watters, 2015).
Complexity of machine learning algorithms means that even their developers are at times unsure as to how said algorithms arrive at particular conclusions
Ethics are rarely an area of focus in instructional design and technology (Gray & Boling, 2016) – and related edtech-focused areas. In designing bots where should we turn for moral guidance? Who are such systems benefiting? Whose interests are served? If we can’t accurately predict how bots may make decisions when interacting with students (see bullet point above), how will we ensure that moral values are embedded in the design of such algorithms? Whose moral values in a tech industry that’s mired with biases, lacks broad representation, and rarely heeds user feedback (e.g., women repeatedly highlighting the harassment they experience on Twitter for the past 5 or so years, with Twitter taking few, if any, steps to curtail it)?

Bots, AI, & Education update #1

By George Veletsianos

On March 21, 2018

In teacherbots

A rough set of notes from today that focus on teacherbots and artificial intelligence in education

Bots in education bring together many technologies & ideas including, but not limited to artificial intelligence, data analytics, speech-recognition technologies, personalized learning, algorithms, recommendation engines, learning design, and human-computer interaction.
- They seek to serve many roles (content curation, advising, assessment, etc)
Many note the potential that exists in developing better algorithms for personalized learning. Such algos are endemic in the design of AI and bots
- Concerns: Black box algorithms, data do not fully capture learning & may lead to biased outcomes & processes
Downes sees the crux of the matter as What AI can currently do vs. What AI will be able to do
- This is an issue with every new technology and the promises of its creators
- Anticipated future impact features prominently in claims surrounding impact of tech in edu
Maha Bali argues that AI work misunderstands what teachers do in the classroom
- Yet, in a number of projects we see classroom observations as being used to inform the design of AI systems
“AI can free time for teachers to do X” is an oft-repeated claim of AI/bot proponents. This claim often notes that AI will free teachers from mundane tasks and enable them to focus on those that matter. We see this in Jill Watson, in talks from IBM regarding Watson applications to education, but also in earlier attempts to integrate AI, bots, and pedagogical agents in education (e.g., 1960s, 1980s). Donald Clark reiterates this when he argues that teachers should “welcome something that takes away all the admin and pain.” See update* below.
Another oft-repeated claim is that AI & bots will work with teachers, not replace them
At times this argument is convincing. At other times, it seems dubious (e.g., when made in instances where proponents ask readers/audience to imagine a future where every child could have instant access to [insert amazing instructor here])
Predictions regarding the impact of bots and AI abound (of course). There’s too many to list here, but here’s one example
“Why a robot-filled education future may not be as scary as you think” argues that concerns around robots in education are to be expected. The article claims that people are “hard-wired” to perceive “newness as danger” as it seeks to explain away concerns by noting that education, broadly speaking, avoids change. There’s no recognition anywhere in the article that (a) education is, and has always been, in a constant state of change, and (b) edtech has always been an optimistic endeavour, so much so that its blind orthodoxy has been detrimental to its goal of improving education.

Update:

From Meet the mind-reading robo tutor in the sky:

And underpaid, time-stressed teachers don’t necessarily have the time to personalize every lesson or drill deep into what each child is struggling with.

Enter the omniscient, cloud-based robo tutor.

“We think of it like a robot tutor in the sky that can semi-read your mind and figure out what your strengths and weaknesses are, down to the percentile,” says Jose Ferreira, the founder and CEO of ed-tech company Knewton.”

Tri-council guidance on using online public data in research

By George Veletsianos

On March 9, 2018

In emerging technologies, NPS, open, scholarship

I am often asked whether there are Canadian ethics guidelines on the use of online public data in research. The relevant section from the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans is provided below. I believe that researchers should take further steps to protect privacy and confidentiality pertaining to public data, but with regards to accessing and using public online data, this is a start.

A sample project to which these guidelines may apply is the following: The researcher will collect and analyze Twitter profiles and postings of higher education stakeholders (e.g., faculty, researchers, administrators) and institutional offices (e.g., institutional Twitter accounts). This research will use exclusively publicly available information. Private Twitter accounts (ie those that are not public and involve an expectation of privacy) will be excluded from the research. The purposes of the research is to gain a better understanding of Twitter metrics, practices, and use/participation.

=== Begin relevant Tri–council guidance ===

Retrieved on December 12 2014 from http://www.pre.ethics.gc.ca/eng/policy-politique/initiatives/tcps2-eptc2/chapter2-chapitre2/

REB review is also not required where research uses exclusively publicly available information that may contain identifiable information, and for which there is no reasonable expectation of privacy. For example, identifiable information may be disseminated in the public domain through print or electronic publications; film, audio or digital recordings; press accounts; official publications of private or public institutions; artistic installations, exhibitions or literary events freely open to the public; or publications accessible in public libraries. Research that is non-intrusive, and does not involve direct interaction between the researcher and individuals through the Internet, also does not require REB review. Cyber-material such as documents, records, performances, online archival materials or published third party interviews to which the public is given uncontrolled access on the Internet for which there is no expectation of privacy is considered to be publicly available information.

Exemption from REB review is based on the information being accessible in the public domain, and that the individuals to whom the information refers have no reasonable expectation of privacy. Information contained in publicly accessible material may, however, be subject to copyright and/or intellectual property rights protections or dissemination restrictions imposed by the legal entity controlling the information.

However, there are situations where REB review is required.

There are publicly accessible digital sites where there is a reasonable expectation of privacy. When accessing identifiable information in publicly accessible digital sites, such as Internet chat rooms, and self-help groups with restricted membership, the privacy expectation of contributors of these sites is much higher. Researchers shall submit their proposal for REB review (see Article 10.3).

Where data linkage of different sources of publicly available information is involved, it could give rise to new forms of identifiable information that would raise issues of privacy and confidentiality when used in research, and would therefore require REB review (see Article 5.7).

When in doubt about the applicability of this article to their research, researchers should consult their REBs.

=== End relevant Tri–council guidance ===

What audiences do academics imagine finding online?

By George Veletsianos

On March 5, 2018

In my research, networked scholars, scholarship, sharing

When online, people draw on the limited cues they have available to create for themselves an imagined audience. This audiences shapes our social media practices and the expression of our identity. While institutions encourage scholars to go online, and many scholars perceive value in online networks themselves, limited research has explored the ways that scholars conceptualize online audiences.

Audiences by NordForsk/Stefan Tell

In a recent paper, we were interested to understand how scholars conceptualize their audiences when participating on social media, and does that conceptualization impacts their self-expression online. Below is a short summary of the results. The full study is here: Veletsianos, G., & Shaw, A. (2018). Scholars in an Increasingly Open and Digital World: Imagined Audiences and their Impact on Scholars’ Online Participation. Learning, Media, & Technology, 43(1), 17-30.

We used a qualitative approach to this study, interviewing 16 individuals who represented a range of academic disciplines and roles. Data were generated from two sources: semi-structured interviews with each participant, and examination of the social media spaces they used (e.g. blogs, Facebook, Twitter).

Participants identified four specific groups as composing their social media audiences: (1) academics, (2) family and friends, (3) groups related to one’s profession, and (4) individuals who shared commonalities with them. Interviewees felt fairly confident that they had a good understanding of the people and groups that made up their audiences on social media, but distinguished their audiences as known and unknown. The known audience included those groups and individuals known to interviewees personally. The unknown audience consisted of members whom participants felt they understood much about but did not know personally. Interviewees reported using their understanding of their audience to guide their decisions around what, how or where to share information on social media. All participants reported filtering their social media posts. This action was primarily motivated by participants’ concerns about how postings would reflect on themselves or others.

The audiences imagined by the scholars we interviewed appear to be well defined rather than the nebulous constructions often described in previous studies. While scholars indicated that some audiences were unknown, none noted that their audience was unfamiliar. This study also shows that a misalignment exists between the audiences that scholars imagine encountering online and the audiences that higher education institutions imagine their faculty encountering online. The former appear to imagine finding community and peers and the latter imagine scholars finding research consumers (e.g., journalists).

Educational Technology Magazine archive (1966-2017)

By George Veletsianos

On February 2, 2018

In papers, scholarship

Larry Lipsitz, the founder and long-time editor of Educational Technology magazine, passed away last year and is missed by many (see the tributes and remembrances many of us wrote in the last issue of the magazine).

With Larry’s insight, Educational Technology published cutting-edge, critical, thoughtful, and important work.

Educational Technology was a print-only publication. However, Howard Lipsitz, Larry’s brother, has collaborated with JSTOR to preserve Larry’s legacy and make all articles available online where they can be read for free. Here’s the Educational Technology magazine archives (1966-2017).

Words to live by

By George Veletsianos

On January 12, 2018

In scholarship

William Pinar’s writing is powerful. This is particular is something worth sharing:

“we scholars must treat each other with the same pedagogical thoughtfulness and sensitivity with which we claim to treat students in our classrooms, and with which we ask our students (prospective and practicing teachers) to treat theirs” (p. 266).

Though Pinar here writes about peer-review, this to me highlights the sort of relationships that institutions of higher education should foster and support.

Kumashiro, K., Pinar, W., Graue, E., Grant, C., Benham, M., Heck, R., … & Luke, C. (2005). Thinking collaboratively about the peer-review process for journal-article publication. Harvard Educational Review, 75(3), 257-285.