Predictive Learning Analytics Reflection
EME 6348, Summer 2024 Heather Corona
#1 SUrprising Nuggets of Knowledge
Nugget #1
Nugget #2
Nugget #3
What to use in the Future
#2
In the future, I would like to use more of the actual data models in various contexts to get a feel for their potential. Insight Software shared how the Classification Model can be used to make predictions from historical data, perhaps predicting customers who may churn as example. The Forecast Model is another lens I would like to explore more in the future, as there are applications in the Instructional Design world when considering content and student engagement. (Insight Software, 2023)
Additionally, I'd like to work more with .sav files and working with software such as SPSS from Module 2. That was very helpful and relevant to my current role, as well as my future endeavor to be an Instructional Designer!
#3 Challenges & Successes
Downloading SPSS
This was a major challenge since my laptop did not have the space, however I was able to turn it into a success and use this wonderful dashboard, AddMaple, that infuses insights and AI opportunities when analyzing large data sets!
#3 Challenges & Successes
Finding Relevant Data
Planning the final project and finding the most befitting data for the predictive model initiative was an interesting challenge! At first I wasn't sure what premise I wanted to support, however after exploring the NCES website (WOW- the accessible data is mind-blowing) I quickly noticed the early childhood survey options. From there, my ideas started to take shape!
#4
Artificial Intelligence
Something I wish I had learned more about is the current impact of AI in PLA, as well as the potential it may have in the near future!
I absolutely enjoyed this course! I don't have a least favorite attribute, however since I have to provide input I'll share that there was one article I had a difficult time reading and connecting to since there were a few typos. A Survey on Predictive Models of Learning Analytics from Module 2, Topic 2, was a tough read and my editing eyes were distracted at times.
I enjoyed collaborating with peers as we engaged in complex texts! This added to my understanding of the content and helped to solidify or change some of my opinions on a topic. Seeing what information peers prioritized or found captivating was very helpful and highlighted shared interests!
Perusall
An Article
Favorites
#5
References
Civitas Learning. (2019). What really works: A review of student success initiatives. Ekowo, M., & Palmer, I. (2016) The promise and peril of predictive learning analytics in higher education. New America. Gardner, L. (2017). Keeping up with the growing threat to data security. The Chronicle of Higher Education. Insight Software. (2023). Top 5 predictive analytics models and algorithms. https://insightsoftware.com/blog/top-5-predictive-analytics-models-and-algorithms/ Lester, J., Klein, C., Rangwala, H. and Johri, A. (2017). Learning analytics in higher education. ASHE Higher Education Report, 43: 74-99. The Wall Street Journal. (2021, July 21). How TikTok's algorithm figures you out [Video]. YouTube. https://www.youtube.com/watch?v=nfczi2cI6Cs
Nugget #1
Predictive Learning Analytics (PLA) have the powerful potential to be used for amazing change, such as USF's implementation that led to a dramatic increase in first-year retention (Civitas Learning, 2019).
Nugget #2
On the flipside, Predictive Learning Analytics have been used for unfortunate reasons like when Mount Saint Mary’s University president used data from a survey to “drown the bunnies” to improve retention rates (Ekowo & Palmer, 2016).
Nugget #3
Algorithms used in PLA may lack transparency, which can lead to bias (Lester, Klein, Rangwala & Johri, 2017) and questionable decisions, such as Tiktok's concerning behavior that is intended to keep people clicking, despite mental health implications (Wall Street Journal, 2021).
Predictive Learning Analytics Reflection, Summer 2024
Heather
Created on June 18, 2024
Start designing with a free template
Discover more than 1500 professional designs like these:
View
Tech Presentation Mobile
View
Geniaflix Presentation
View
Vintage Mosaic Presentation
View
Shadow Presentation
View
Newspaper Presentation
View
Zen Presentation
View
Audio tutorial
Explore all templates
Transcript
Predictive Learning Analytics Reflection
EME 6348, Summer 2024 Heather Corona
#1 SUrprising Nuggets of Knowledge
Nugget #1
Nugget #2
Nugget #3
What to use in the Future
#2
In the future, I would like to use more of the actual data models in various contexts to get a feel for their potential. Insight Software shared how the Classification Model can be used to make predictions from historical data, perhaps predicting customers who may churn as example. The Forecast Model is another lens I would like to explore more in the future, as there are applications in the Instructional Design world when considering content and student engagement. (Insight Software, 2023)
Additionally, I'd like to work more with .sav files and working with software such as SPSS from Module 2. That was very helpful and relevant to my current role, as well as my future endeavor to be an Instructional Designer!
#3 Challenges & Successes
Downloading SPSS
This was a major challenge since my laptop did not have the space, however I was able to turn it into a success and use this wonderful dashboard, AddMaple, that infuses insights and AI opportunities when analyzing large data sets!
#3 Challenges & Successes
Finding Relevant Data
Planning the final project and finding the most befitting data for the predictive model initiative was an interesting challenge! At first I wasn't sure what premise I wanted to support, however after exploring the NCES website (WOW- the accessible data is mind-blowing) I quickly noticed the early childhood survey options. From there, my ideas started to take shape!
#4
Artificial Intelligence
Something I wish I had learned more about is the current impact of AI in PLA, as well as the potential it may have in the near future!
I absolutely enjoyed this course! I don't have a least favorite attribute, however since I have to provide input I'll share that there was one article I had a difficult time reading and connecting to since there were a few typos. A Survey on Predictive Models of Learning Analytics from Module 2, Topic 2, was a tough read and my editing eyes were distracted at times.
I enjoyed collaborating with peers as we engaged in complex texts! This added to my understanding of the content and helped to solidify or change some of my opinions on a topic. Seeing what information peers prioritized or found captivating was very helpful and highlighted shared interests!
Perusall
An Article
Favorites
#5
References
Civitas Learning. (2019). What really works: A review of student success initiatives. Ekowo, M., & Palmer, I. (2016) The promise and peril of predictive learning analytics in higher education. New America. Gardner, L. (2017). Keeping up with the growing threat to data security. The Chronicle of Higher Education. Insight Software. (2023). Top 5 predictive analytics models and algorithms. https://insightsoftware.com/blog/top-5-predictive-analytics-models-and-algorithms/ Lester, J., Klein, C., Rangwala, H. and Johri, A. (2017). Learning analytics in higher education. ASHE Higher Education Report, 43: 74-99. The Wall Street Journal. (2021, July 21). How TikTok's algorithm figures you out [Video]. YouTube. https://www.youtube.com/watch?v=nfczi2cI6Cs
Nugget #1
Predictive Learning Analytics (PLA) have the powerful potential to be used for amazing change, such as USF's implementation that led to a dramatic increase in first-year retention (Civitas Learning, 2019).
Nugget #2
On the flipside, Predictive Learning Analytics have been used for unfortunate reasons like when Mount Saint Mary’s University president used data from a survey to “drown the bunnies” to improve retention rates (Ekowo & Palmer, 2016).
Nugget #3
Algorithms used in PLA may lack transparency, which can lead to bias (Lester, Klein, Rangwala & Johri, 2017) and questionable decisions, such as Tiktok's concerning behavior that is intended to keep people clicking, despite mental health implications (Wall Street Journal, 2021).