WEBVTT Kind: captions Language: en 00:00:00.080 --> 00:00:03.920 Okay thank you so much, Katie. So as Katie said,   00:00:03.920 --> 00:00:08.320 my name is Nikki Sochacka and I'm presenting on  behalf of my team at the University of Georgia. 00:00:09.600 --> 00:00:16.240 So the main research question that we examined  in our project was how did students, faculty, and   00:00:16.240 --> 00:00:21.840 staff in a college of engineering experience the  COVID-19 crisis and transition to online learning? 00:00:22.640 --> 00:00:28.240 So we used a novel approach called SenseMaker to  do this work. SenseMaker is a method designed to   00:00:28.240 --> 00:00:33.920 inquire into and change complex social systems.  So our College of Engineering is an example of   00:00:33.920 --> 00:00:40.080 such a system. SenseMaker does this by collecting  stories from within the system and then posing the   00:00:40.080 --> 00:00:46.160 question: what changes can we make to create more  stories like this and fewer stories like that? Or   00:00:46.160 --> 00:00:51.840 put another way: how can we amplify positive  experiences and dampen negative experiences? 00:00:53.600 --> 00:00:58.720 SenseMaker has been described as a mixed  method that combines the power of first-hand   00:00:58.720 --> 00:01:04.160 narratives with the statistical authority of  quantitative data. So as I mentioned earlier,   00:01:04.160 --> 00:01:12.160 narratives, or short stories, are what make up  the qualitative data in a SenseMaker project. 00:01:12.160 --> 00:01:18.880 So they- so these narratives are collected  via a prompt that looks something like this:   00:01:18.880 --> 00:01:23.840 so tell us about something you've recently  experienced. So the quantitative data comes   00:01:23.840 --> 00:01:28.560 from how participants make sense of their own  stories. So participants do this by answering   00:01:28.560 --> 00:01:32.800 a series of questions that are part of  what's called a signification framework. 00:01:33.840 --> 00:01:39.040 So the signification frameworks combine or  comprise three different types of questions:   00:01:39.040 --> 00:01:41.360 triads, dyads, and multiple choice questions. 00:01:42.240 --> 00:01:47.040 This is an example of a triad. So  after participants tell their stories,   00:01:47.040 --> 00:01:52.240 they make sense of their own stories by moving  the dot on the triad to the position that best   00:01:52.240 --> 00:01:58.080 fits with their story. When we see the data on  the analyst software side, it looks like this.   00:01:58.800 --> 00:02:05.440 So each dot represents one story. So we can use  that software to highlight clusters of stories.   00:02:05.440 --> 00:02:09.760 So, for example, here I have selected  stories in the grit and perseverance   00:02:09.760 --> 00:02:15.920 corner of the triad. The titles of the story  on the left hand side are what I see when I   00:02:15.920 --> 00:02:20.000 click on one of these titles, so this allows  me to read the entire participant's story. 00:02:21.280 --> 00:02:24.480 The second type of question  in the signification framework   00:02:24.480 --> 00:02:28.640 is called a dyad, and this is an example. And  these work in the same way. Participants move   00:02:28.640 --> 00:02:31.920 the dot to the position on the  dyad that fits with their story. 00:02:32.800 --> 00:02:36.160 And here are some examples  of multiple choice questions.   00:02:36.160 --> 00:02:39.520 Participants' responses to these  questions can be used to filter the data.   00:02:40.640 --> 00:02:44.160 So how did our college experience  the transition to online learning? 00:02:44.880 --> 00:02:52.000 So we collected 71 stories in the spring of 2020  and a further 71 in the fall. In the spring,   00:02:52.000 --> 00:02:57.520 the majority of the faculty and staff  stories were positive. Unfortunately,   00:02:57.520 --> 00:03:02.400 the majority of student stories were negative.  When we looked closely at the data, we saw that   00:03:02.400 --> 00:03:07.920 one possible explanation for this was that faculty  had agency in how they responded to the crisis.   00:03:09.040 --> 00:03:12.880 Yes, they had to go online, but they  could decide what that looked like.   00:03:12.880 --> 00:03:17.920 Students, on the other hand, were on  the receiving end of these changes. 00:03:17.920 --> 00:03:21.680 So SenseMaker also allows for  more advanced visualizations,   00:03:21.680 --> 00:03:25.520 like this one, which can point to  opportunities for positive change.   00:03:26.240 --> 00:03:31.760 So this visualization of the data is called a  heat map, and it comes from combining participant   00:03:31.760 --> 00:03:35.760 responses to two questions. It's actually the  dyad and the triad that I showed you earlier.   00:03:37.040 --> 00:03:41.200 So here, in the top left hand corner, we can  see a concentration of stories that participants   00:03:41.200 --> 00:03:48.240 rated as high struggle and low praise by those in  power. And here's another concentration of stories   00:03:48.240 --> 00:03:53.360 that are low struggle and high praise  by those in power. So the question is:   00:03:53.360 --> 00:03:59.360 what can we do in real time to create more  stories like this, so the bottom right hand side,   00:03:59.360 --> 00:04:05.440 and less stories like are at the top? So to answer  this question, we can study the story so the   00:04:05.440 --> 00:04:11.600 actual experiences the participants recounted. And  I'm going to share one of those stories right now. 00:04:13.120 --> 00:04:14.560 So I'll give you a few seconds   00:04:14.560 --> 00:04:16.875 to read the highlighted parts of the story. [Text reads as below, highlighted text in italics:   00:04:16.875 --> 00:04:17.308 “In a typical semester, finals week is often  pretty grueling. Engineering professors almost   00:04:17.308 --> 00:04:17.686 always give 3-hr exams for their final… …the outbreak caused the professors to   00:04:17.686 --> 00:04:18.110 reorganize their finals… rather than a tough  exam for a engineering elective-level class,   00:04:18.110 --> 00:04:18.552 he made it a project with several options. We  could write a report on engineering case studies,   00:04:18.553 --> 00:04:18.986 write a critique of a chapter from a textbook  he was working on, or solve an extended problem   00:04:18.986 --> 00:04:19.405 using both analytical and numerical methods. What amazed me is the breadth of thee project.   00:04:19.405 --> 00:04:19.820 Since students have different strengths, they  can choose the option that best compliments   00:04:19.820 --> 00:04:20.240 their abilities, and I would like to see this  sort of project format in future courses.”]  00:04:20.240 --> 00:04:25.440 So here we see a possibility for amplifying a  positive experience in our system. So we shared   00:04:25.440 --> 00:04:30.240 this story with our faculty to provide  examples of alternatives to final exams   00:04:30.240 --> 00:04:34.960 in online environments. So of course there  were negative stories too. In these stories,   00:04:34.960 --> 00:04:39.520 students spoke about isolation, lack of  flexibility, internet connection problems,   00:04:39.520 --> 00:04:44.320 COVID-19 cases in the family, and more. So  this is just a taste of what we found in the   00:04:44.320 --> 00:04:49.200 spring data. What about what happened in the  fall? Unfortunately, we saw a startling shift   00:04:49.200 --> 00:04:54.400 towards more stories of struggle and less of  those which showed praise by those in power. 00:04:54.960 --> 00:05:00.000 So this is that same heat map that I showed  you earlier this time created using MATLAB. 00:05:00.000 --> 00:05:07.440 Here is that same heat map from the fall. Here  we can clearly see two concentrations of stories   00:05:07.440 --> 00:05:12.800 have now all clustered around much higher  struggle and low praise by those in power.   00:05:14.080 --> 00:05:19.040 So what happened? What changed from the spring  to the fall? So I've described our college as a   00:05:19.040 --> 00:05:23.280 social system. One explanation is that in  the fall the university system in Georgia   00:05:23.280 --> 00:05:28.000 mandated in-person learning through a hybrid  teaching model. This requirement undermined   00:05:28.000 --> 00:05:32.480 faculty and student agency and how they wish to  engage in instructional activities in the fall.   00:05:33.040 --> 00:05:36.000 This and other findings from our  spring and fall data are available   00:05:36.000 --> 00:05:40.320 in four reports we've published as part of  our RAPID grant, which are available here. 00:05:40.320 --> 00:05:46.400 Thank you and I look forward to your  questions at the end of this session.