Agenda What is Learning Analy1cs defini1on Introduc1on to Pa9ern - - PowerPoint PPT Presentation

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Agenda What is Learning Analy1cs defini1on Introduc1on to Pa9ern - - PowerPoint PPT Presentation

Oct 05, 2022 463 likes •1.13k views

UW Madison Learning AnalyEcs Series U SIN G P ATTERN TO L O G C O U RSE A CTIVITIES P RESEN TED BY M IG U EL G A RCIA -G O SA LVEZ H EATH ER K IRKO RIA N J A M ES M C K AY K IM A RN O LD I NTRODUCTION TO L EARNING A NALYTICS AND P ATTERN Agenda

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USIN G PATTERN TO LO G CO U RSE ACTIVITIES

PRESEN TED BY

MIG U EL GA RCIA-GO SA LVEZ HEATH ER KIRKO RIA N JA M ES MCKAY KIM ARN O LD

UW Madison Learning AnalyEcs Series

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INTRODUCTION TO LEARNING ANALYTICS

AND PATTERN

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Agenda

What is Learning Analy1cs – defini1on Introduc1on to Pa9ern – quick demo Instructor Perspec1ves – Heather Kirkorian, SOHE – Miguel Garcia-Gosalvez, WSoB Student Perspec1ve & Evalua1on Discussion

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What is Learning Analytics?

Learning analy2cs is the measurement, collec2on, analysis, and repor2ng of data about learners and their contexts, for the purposes of understanding and op2mizing learning and the environments in which it occurs.

~ Society for Learning Analy2cs Research

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Do Reflec(ve Prac(ces Impact Learning?

Does logging study (me impact the learning experience? How do students feel about sharing their data (if it helps them learn)? Are students willing to change study habits based on personalized feedback from a learning analy(cs tool? What can instructors learn... about students… about courses… about how they teach?

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Courtesy of Rachelle DiGregorio

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Pattern — Quick Intro/Review

Interac4ve study log (sort of like a FitBit for studying)

  • Students track study and learning behavior/ac4vi4es,
  • and rate their produc4vity.

Instructors only see aggregate

  • data (unless student chooses

to share their personal data with instructor/advisor) LMS agnos4c

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Pa#ern — Quick Intro/Review

  • Mobile app & website that creates and curates data
  • More holistic student behavior patterns
  • Course-level, by student
  • Licensed by Purdue University
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Students Log Time Spent

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Instructor Dashboard - Aggregated Student Data

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UW Madison Learning Analytics Series

PATTERN IN ACTIO N: HEATH ER KIRKO RIAN

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Students need an incen,ve…

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Spring 2017 – no bonus points

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Fall 2016 – 2 bonus points

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Define categories

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  • Lecture: Completing online modules, including mini-

lectures, videos, and activities (e.g., practice quizzes, matching games, demos)

  • Assigned reading: Reading chapters in the textbook
  • Discussions: Preparing original responses and peer

responses for discussion assignments; this can include time reading articles in prompts

  • Studying: Reviewing any materials to prepare for a quiz

(e.g., re-reading chapters, re-watching lectures/videos, making flashcards)

  • Office hours: Visiting instructor/TA office hours in person
  • r be phone, or seeking assistance using online resources,

such as posting to the Q&A discussion forum or emailing with the instructor/TA

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Percent of *me in each ac*vity

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Average (me in each ac(vity

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Average is be+er than average?

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When do students work?

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Tuesdays

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Monday/Tuesday

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Thursday/Friday

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UW Madison Learning Analy2cs Series

PATTERN IN ACTIO N: MIG U EL GARCIA-GO SALVEZ

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GEN-BUS 311: Fundamentals of Management and Marketing for Non-Business Majors

Completely redesigned 3

  • credit course moved from face-

to-face to online format. Need a way to validate assump:ons about workload

  • (make sure that the hours involved pass the

requirements). Need to understand student behavior in the online

  • environment.

Provide students ongoing point of reference

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PATTERN IN GENBUS 311 (ONLINE)

FOR INSTRUCTORS

  • Data to validate assump/ons (when conver/ng from
  • F2F to Online)

Pa=ern Recogni/on

  • FOR STUDENTS
  • Individual recogni/on of behavior
  • Comparison to peers
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PATTERN IN GENBUS 311 (ONLINE)

Reward Students Completed Total FALL 2016 Extra Credit 5% 96 259 37.07% SPRING 2017 Extra Credit 2% 69 285 24.21% FALL 2017 1% Final Grade 72 228 31.58%

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IMPACT OF PATTERN ON GRADE GB311-FALL2017

Grade # Students % Students Not Counting Pattern % Students A 66 29.07% 60 26.43% AB 72 31.72% 77 33.92% B 50 22.03% 51 22.47% BC 29 12.78% 29 12.78% C 6 2.64% 6 2.64% D 0.00% 0.00% F 4 1.76% 4 1.76% TOTAL STUDENTS 227 100.00% 227 100.00%

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DISCUSSION (in my context)

It helps the good students

  • Do they just take it as just another assignment?
  • Does Pa8ern incen:vize lower end students being
  • able to compare themselves with peers?

It rewards persistence, commitment, organiza:on,

  • etc. Good skills to have and part of learning outcomes

Those who complete the requirements are those

  • who already do this anyway in all other parts of the

course.

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UW Madison Learning Analy2cs Series

STUDENT PERSPECTIVE

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UW Madison Learning Analy2cs Series

PATTERN EVALUATION

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N=1,744

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N=1,748

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N=1,746

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N=1,766

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N=1,748

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N=1,744

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Evidence of Reflec-ve Learning

“I learned I needed to study in smaller sec-ons more o:en rather than study for 4 hours straight” “...helped me recognize what -mes of day were best to devote study -me to and if I was studying enough. It also helped me es-mate how much I needed to study for exams to perform compara-vely well based on past -me studying for exams” “I used it to see how much -me I spent on something in hindsight to predict how much -me I'll spend on the task in the future. For example, I found out that I usually spend 24 hours studying for a

  • midterm. ”
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Other Comments

“Pa/ern held me accountable for the work that I put into this course” “It was almost like a game, and it encouraged me to study and do my homework”

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Other Comments

“I quickly stopped using the app when it con:nued to tell me that I needed to do more work because I was behind my classmates… I felt it beli>led my studying, and in fact my grades have been far above others” “It felt like a shaming tool”

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Thank you!

Ques.ons? james.mckay@wisc.edu miguel.garcia@wisc.edu kirkorian@wisc.edu kimberly.arnold@wisc.edu