Here is the word cloud for work I co-authored, titled, “Integrating Learning Analytics to Measure Message Quality in Large Online Conversations,” which was presented at the 53rd Hawaii International Conference on System Sciences on January 10, 2020. It received a best paper nomination. This work is a collaborative effort with Asst. Professor Evren Erylimaz.

The paper abstract is as follows:

Abstract: Research on computer-supported collaborative learning often employs content analysis as an approach to investigate message quality in asynchronous online discussions using systematic message-coding schemas. Although this approach helps researchers count the frequencies by which students engage in different socio-cognitive actions, it does not explain how students articulate their ideas in categorized messages. This study investigates the effects of a recommender system on the quality of students’ messages from voluminous discussions. We employ real-time learning analytics to produce a quasi-quality index score for each message. Moreover, we examine the relationship between this score and the phases of a popular message-coding schema. Empirical findings show that a custom CSCL environment extended by a recommender system supports students to explore different viewpoints and modify interpretations with higher quasi-quality index scores than students assigned to the control software. Theoretical and practical implications are also discussed.

Citation
E. Eryilmaz, B. Thoms, KH Lee, M. de Castro, “Integrating Learning Analytics to Measure Message Quality in Large Online Conversations,” Proceedings of Hawaiian International Conference on System Sciences (HICSS 53), January 7-10, 2020, Wailea, HI, USA. 

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For Fall 2019, I added a new module to the dynamic quality analyzing discussion board and allows users to thumbs-up / thumbs-down and bookmark posts for later. The idea is to integrate the data back into the sites recommender engine, which will showcase relevant content from across the site.

After testing across multiple devices and platforms, I noticed a bug in the recommender panel, which prevents interfacing from iOS devices. Very frustrating! Especially since it works on every other platform including Mac OS. Initially, I wasn’t worried, because how many students would be accessing their coursework through a smartphone, right? Well, it turns out that quite a few will be. A pretest survey found that around 40% of students would be accessing the system through iOS (see below). Therefore, I thought it might be important to correct the bug.

After debugging, I discovered that the bug is common across iOS devices. For some jquery click events, the css needed to be modified to include the attribute cursor: pointer. Very frustrating since android devices work fine. That said, I should have looked the bug up a bit earlier in the debugging process, but all is right as rain.

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I had a great time feeling like a diva and giving a seminar at the Summer Taste of OLLI series.

I did my best to present on a topic that really isn’t my specialty, but I believe it’s important to educate people on where we get information and how well we can trust that information. We covered topics such as Information Disorder, which consists of a combination of misinformation and disinformation. We also discussed the role social media plays in spreading fake news and the role of fake news in our political systems and what measures folks can do to mitigate the amount of disinformation they receive… In one word, Snopes.com. Here is the abstract for the talk:

The subject of “fake news” has arisen again and again before and after the 2016 election, but the concept has actually been around for centuries. This Taste of OLLI lecture will offer a brief history of fake news — the deliberate publication of misinformation and hoaxes in the mainstream media. In the course of this talk, we will receive tips for verifying information and sources and explore how social media has affected the “fake news” phenomenon.

Here is a snap of me trying to figure out how to wear a headset.

More on OLLI can be found here: https://ext.csuci.edu/programs/osher-lifelong-learning-institute/index.htm

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I noticed something interesting about Waze the other day, which confirmed some suspicions I had for a few weeks.

For some time, visitors keep getting lost getting to my house, which sits on a cul de sac near the Los Angeles coastline. On more than one occasion, people got lost when using the Waze app, which is supposedly the best crowdsourced navigation around. The app would direct them down deadend streets, blocks away from my home. As it turns out, this may be my fault (not really, but really)…

I witnessed this phenomenon first hand the other night returning from a wedding at the Aquarium of the Pacific. It was about midnight that I noticed my Uber driver turning down a deadend street near my home. When I asked where the driver was going, the directions stated, in 450 feet, make a left on unnamed street. He proceeded to miss the turn.

This is the route I usually take, but I was surprised that Waze would recommend this to an Uber driver. The road is unnamed because it’s not a road, but the back alley of my townhome’s complex. The back alley connects two streets by about a 1/4 mile stretch marked with speed bumps and blindspots.

My suspicion is that because I take this route so frequently and Waze tracks me so closely, Waze thinks that this is now the optimal route. Problem is that it’s only optimal if you know about the alley, not if you miss the turn. Go figure…

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Here is the Wordle.net word cloud for work I co-authored, titled, “Affordances of Recommender Systems for Disorientation in Large Online Conversations,” which was published in the Journal of Computer Information Systems. This work is another collaborative effort with Asst. Professor Evren Erylimaz.

ABSTRACT
In the context of large annotation-based literature discussions, this research examines the affordances of recommender systems on users’ disorientation. Drawing insights from literature on group cognition, knowledge building, and recommender systems, we developed three recommender systems and tested these systems on 136 users. Results indicate that the recommender system with constrained Pearson correlation coefficient similarity metric reduced users’ disorientation and afforded them the opportunity to become better aware of interesting and relevant information based on their needs and preferences without heavy costs in terms of time and effort. With respect to other software conditions, results indicate that users suffered from higher levels of disorientation. These findings counter the claim that annotations reduce disorientation. Theoretical and practical implications are also discussed.

E. Eryilmaz, B. Thoms, Z. Ahmed, KH Lee, “Affordances of Recommender Systems for Disorientation in Large Online Conversations,” Journal of Computer Information Systems, April, 2019. (Impact Factor 1.56)

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