Home » Articles posted by Dorina Tila (Page 4)
Author Archives: Dorina Tila
Notes from Meeting 1 of Summer 2020 (Jun 22, 2020 1:00 – 2:00 PM)
During the summer of 2020, the Data Faculty Interest Group (FIG) with support from the Office of Institutional Effectiveness, and therefore with institutional commitment, designed, implemented, and analyzed the results of a student survey and a faculty survey. Singular in purpose, this survey measured the Spring 2020 Emergency Remote Instruction (ERI) student experiences. ERI is differentiated from the traditional and optional online model practiced before Spring 2020 as it was an unexpected situation leaving neither students nor faculty with preparatory pedagogical time or choice.
The group collected the data from the two surveys sent to students and faculty at CUNY Kingsborough Community College during June 2020 and conducted analysis of the data. For more information about findings, please contact the facilitator: [email protected] or join us in the Fall 2020 meetings.
Notes from Meeting 5 of Spring 2020 (June 3, 2020 12:00pm -1:00pm)
The group finalized the faculty survey. The following steps were setup.
- June 4 -5: Finalize the introduction of the surveys
- June 8: Deploy the surveys.
- June 15, 17: Send reminders to complete the survey
- June 19: Surveys will be closed and we will be ready to analyze the data.
- June 19 – July 31: Since time is of essence in analyzing the data, many of us expressed interest to meet in the summer. I will send a doodle next week and it would be helpful if you send me some preference (e.g., days, time of the day)
Notes from Meeting 4 of Spring 2020 (May 27, 2020 12:00pm -1:00pm)
During this meeting we will discuss and review the daft surveys (see attached below).
SurveyMonkey_284595543 updates as of 5-24-20
Student Survey comments as of 5-24-2020
Topic: KCTL Data FIG meeting
Time: May 27, 2020 12:00 PM Eastern Time (US and Canada)
Notes from Meeting 3 of Spring 2020 (May 20, 2020 1:00pm -2:00pm)
During this meeting, the faculty will be providing feedback on the updated survey.
Draft Surveys for Faculty and Students about Spring 2020 Conversion to ERI – DATA FIG 5-13-20 v3 (1)
Below you will also find some findings from a recent survey administered by Top Hat to over 3,000 students. Please take a look at the questions and lets discuss if there is anything else we want to add to our current draft. We are aiming at collecting actionable data that will be useful to make changes in the future.
https://tophat.com/blog/adrift-in-a-pandemic-survey-infographic/
Notes from Meeting 1 of Spring 2020 (April 29, 2020 2:00pm -3:00pm)
Initially, the group shared the experience of prior crisis and what their perceived effects had been in teaching. A short discussion followed regarding suggestions offered by some selected articles (please see source below). For example, it is recommended to reduce cognitive load in times of crisis, assign relevant activities or material, talking directly to students, etc.
Suggestions were made to administer a survey to students and faculty to find out about their experience during emergency remote teaching during Spring 2020. Prior to drafting the surveys, we need to clarify the goal. Is the goal to collect data that are actionable for future online teaching or helps us better prepare for future potential crisis. We will return to this discussion in our second meeting on Wed, May 13, 2pm.
I look forward to seeing all on Wednesday, May 13th at 2. Below is Zoom information on all future meetings.
1. Join by Computer or Phone APP
https://us02web.zoom.us/j/7283893643?pwd=SURGZFNzcFl4Q3ZsUmNCMWNHR0VPUT09
If prompted for a password, use 2020
2. Join by dialing the phone
Dial: +16465588656
When prompted for meeting ID, please type 728 389 3643 #
If prompted for a password, use 2020 When prompted for user ID, just type #
Attachments
- Agenda and preparation notes for meeting 1 – Click: Big Data – Covid-19 Meeting April 29-2020 (pdf)
- Draft of student and faculty survey (WIP) – Click: Draft Surveys for Faculty and Students about Spring 2020 Conversion to Emergency Remote instruction – LT DT
- PDF on what type of data faculty can already retrieve from Blackboard – Click – Bb Analytics ONLY PPT 5-8-2020
Sources
- Center for Teaching Vanderbilt University. (2013). “Teaching in Times of Crisis.” Retrieved from https://cft.vanderbilt.edu/guides-sub-pages/crisis/
- Hodges, C., et al. (2020). The difference between emergency remote teaching and online learning. Educause Review. Retrieved from https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning
- McKinsey & Company. (2020). “Coronavirus and the campus: How can US higher education organize to respond?” Retrieved from https://www.mckinsey.com/industries/public-sector/our-insights/coronavirus-and-the-campus-how-can-us-higher-education-organize-to-respond
- National Education Association. (2018).“NEA’s School Crisis Guide,” Retrieved on March 24, 2020 from nea.org/assets/docs/NEA%20School%20Crisis%20Guide%202018.pdf
Notes from Meeting 2 of Spring 2020 (May 13, 2020 2:00pm -3:00pm)
During this second meeting, we continued discussion on what we want to know from faculty and students, what type of data would be useful and actionable. The following survey is a draft including comments.
Draft Surveys for Faculty and Students about Spring 2020 Conversion to ERI – DATA FIG 5-13-20 v3 (1)
Attachments
- Agenda and preparation notes for meeting 1 – Click: Big Data – Covid-19 Meeting April 29-2020 (pdf)
- Draft of student and faculty survey (WIP) – Click: Draft Surveys for Faculty and Students about Spring 2020 Conversion to Emergency Remote instruction – LT DT
- PDF on what type of data faculty can already retrieve from Blackboard – Click – Bb Analytics ONLY PPT 5-8-2020
Notes from Meeting 3 of Fall 2019 (Dec 4, 2019 1:50pm -2:50pm)
The discussion is focused on the difference of data driven vs. data informed decisions (Chapter 7, 8, and online resources below)
The difference between data driven and data informed decisions were discussed. The data driven decision are entirely evidence-based. The importance that is set to the data will also encourage information to be up to date and well examined. However, the negative aspect of it is that data is trusted blindly and little focus is given to qualitative data. Trusting data blindly may lead to negative effects when data is low quality or inaccurate.
On the other hand, making data informed decisions means that data is used as an important factor in decision but not the only factor. It means that qualitative and subjective information is used. A data informed educational institution would take other factors like subjective student experience into consideration in addition to data when making decisions. The advantages of this approach is that it takes into account the limitations of available data, uses multiple sources to make decisions, rather than just relying on data, and encourages creative and “out-of-the-box” decision making process, potentially leading to better results.
We will continue this discussion in Spring 2020.
Resources on data driven v. data informed decisions
Notes from Meeting 2 of Fall 2019 (Nov 13, 2019 1:50pm -2:50pm)
The discussion is focused on the following:
– Leading and Lagging Indicators (Chapter 4, 5, and 6 and online resources below)
– Discussing current indicators used at KCC
– Other colleges & suggestions.
Resources on Lagging & Leading Indicators of Student Performance
– Authors’ blog about the book: Creating a Data-Informed Culture at Community Colleges, by Brad C. Phillips and Jordan Horowitz on August 30,2017. https://www.hepg.org/blog/moving-the-needle-on-community-college-student-suc
– Authors’ presentation http://www.linkedlearning.org/wp-content/uploads/2017/02/Measuring-Success-Leading-and-Lagging-Indicators-for-Linked-Learning.pdf
– Data First: Leading and Lagging Indicators. [Video file.] Retrieved from Center for Public Education, https://vimeo.com/15739964
Other Resources for Current & Future Discussion
– McComb, B. E., & Lyddon, J. W. (2016). Understanding the effectiveness and impact of student success interventions on campus: Understanding the effectiveness and impact of student success. New Directions for Community Colleges, 2016(175), 83-94.
– Carales, V. D., Garcia, C. E., & Mardock‐Uman, N. (2016). Key resources for community college student success programming. New Directions for Community Colleges, 2016(175), 95-102.
– Calculating Cost-Return on Investments in Student Success https://jfforg-prod-prime.s3.amazonaws.com/media/documents/ISS_brief_010510.pdf
– “This pilot project tied program-level cost data to student outcomes and explored the extent to which the additional revenue that colleges and universities generate by increasing student retention offsets the additional cost of first-year programs. The project’s goal was to develop, test, and standardize tools that document the relationship between program costs and student results. Armed with this information, institutions will be better able to make informed, data-driven decisions about how to invest limited dollars in ways that help students succeed.”
https://www.jff.org/resources/calculating-cost-return-investments-student-success/
Additional attachments:
McComb_et_al-2016-New_Directions_for_Community_Colleges (1)
Carales_et_al-2016-New_Directions_for_Community_Colleges (1)
Notes from Meeting 1 of Fall 2019 (Oct 30, 2019 1:50pm -2:50pm)
Faculty Interest Group “Using Data to Support Teaching and Learning”
Source: Creating Data-Informed Culture in Community Colleges: A New Model for Educators
Meeting 1: Chapter 1 to 3
This book shows how to use and present data at the local level to inform decision making and to help local CC to use existing data in ways that lead to improvements in student success. The first meeting will cover the first three chapters which elaborate on analytics block of the Figure below.
The challenge is not getting more data; getting the right data, or having access to data. While they are important, the effective data use to make reframing analytics to make data useful, usable, and actionable.
Data Accuracy
- Data entry
- “…the US Department of Education estimates that “at least 70% and often 80 to 85% of the effort in data analytics is devoted to data cleaning, formatting, and alignment.”
- Data that is send out both internal and external sources needs to be consistently reviewed… (e.g., judgement about how to code data is on IT staffer…)
- Data reporting
- Data needs to be transformed in to tables, charts, and other displays in order to help educators understand the meaning behind numbers (p. 24).
- Putting a good data-use practice to work
- Rule 1: Focus
- Rule 2: Only report data needed for compliance or making a decision
To avoid information overload, focusing on the issue at hand is crucial (37)
- Limit the number of displays to two for each point being made… two different displays
- Reduce the amount of information in any single display
- Tell a story – stories help us connect emotionally as well as cognitively with data. “Numbers, tables, and charts in and of themselves are not enough to inform and influence. Rather, it is the story behind the numbers that has the ability to impact educators.” (p18).
- (10% or 10,000 students did not achieve a goal).
- Put a face on the numbers
- Rule 3: Use telling headlines (p. 25 – 29)
- Rule 4: Use template for engaging in data conversations
- Template – if information is presented the same way in each report, in the same order, with the same heading, it is easier to identify trends within the report.
- …establishes a pattern for presenting information repeatedly, in in that way, capitalizes on the people desire for familiarity, is known as template.
- Provides a natural way to organize information that makes new ideas and are more familiar.
- Templates allow the audience to focus on content and not on the image in front of them (p39).
- Coordination of Reporting
- How does a CC meet the needs of the user and produce data that is not ultimately discounted? We recommended that A&R, IS and IR come together and decide who provides what data to whom… We suggest creating a grid that details who does what for whom and when they do so.
Questions
- How is coordination of reporting in KCC?
- Which model does KCC follow?
- Model 1: The college does not appreciate data and its sues
- Model 2: The college wants to do something with data but is unsure what to do
- Model 3: The college has embraced the use of data but has et to realize its potential.
- Relevant experience and suggestions
Notes from Meeting 3 of Spring 2019 (May 23, 2019 1:50pm -2:50pm)
Faculty Interest Group “Using Data to Support Teaching and Learning”
Notes from Meeting 3 (May 23, 2019 1:50pm -2:50pm)
Data FIG Resources for Automation, Future of Work and Higher Education