Can Faculty Development Utilize Intelligent Tutors?
Consider innovative approaches for faculty development, like using intelligent tutors. Explore solutions to declining attendance at traditional sessions, advocating for online modules, just-in-time approaches, and short webinars. Embrace digital advancements to enhance education.
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Presentation Transcript
Can faculty development be done with intelligent tutors? Janet Corral, PhD
Todays session is a Works In Progress + Active Learning You have prior knowledge (Adult learning theory) You can critically appraise, discuss & build on your knowledge (Constructivist learning theory) People learn best with pauses, discussion, engagement (Active Learning)
Case Study: CU SOM Attendance at traditional in-person faculty development sessions is declining Faculty development is still needed Faculty want to be great educators Concerns persist (e.g. professionalism) Lots of educational advances to learn (e.g. active learning) We live & work in a digital world
Case Study Your Turn! You are a leader on campus. What would you advocate for with respect for faculty development?
Successive Attempts Attempt #1: Online Modules Faculty Development Online Module Usage (2017) 140 120 Number of Uses 100 80 60 40 20 0 TBL for IPE Giving Effective Feedback Medical Student Assessment Teachers' Portfolio Module Title Professionalism Mentoring Struggling Learner Bedside Teaching
SAMR model Puentedura (2013)
Just-in-Time Approaches Right before doing an activity = cognitive prompt (e.g Nishisaki, 2010) During an activity = cognitive support (Collins et al, 1997; Foale et al, 2005)
SAMR model Puentedura (2013)
Successive Attempts Attempt #2: Best Evidence Medical Education (1 pg) 6 faculty users 2 committee meetings No further uptake
Successive Attempts Attempt #3: Short Webinars (30 mins) 5 offerings Time: 12:15-12:45 Active engagement format with dialogue Avg Rating: 4.9/5.0
Successive Attempts Attempt #4: Email - Short Evidence-Based Tips Evidence-based tips Video: demo of steps Video: demo w/ learner
Just-in-Time Coaching Day 3 Day 4 Day 2 Day 1 Email Tip 3 Email Tip 4 Email Tip 2 Email Tip 1
Successive Attempts Attempt #4: Email Short Evidence-Based Tips UME Area Duration # of # of Sub- Topics Average Opening Rate Range Sendouts Family Med & Int Med Clerkship 12 months 12 5 31.5% 10-59% Pathology Small Groups 4 months 6 6 30.8% 15.4-53.8% Colorado Springs Branch 12 months 1 1 35.8% n/a Int Med Clerkship 6 months 6 5 42.2% 20-80%
Successive Attempts Attempt #4: Email Short Evidence-Based Tips UME Area Duration # of # of Sub- Topics Average Opening Rate Range Sendout Cycles Family Med & Int Med Clerkship 12 months 12 5 31.5% 10-59% Pathology Small Groups 4 months 6 6 30.8% 15.4-53.8% Colorado Springs Branch 12 months 1 1 35.8% n/a Int Med Clerkship 12 months 18 5 42.2% 20-80%
Case Study Your Turn! What data from Attempts #1-4 would you use to guide your decision on how to proceed with faculty development?
Intelligent Tutors Computer- assisted learning Expert systems Artificial intelligence Yazdani, 1987; Nwana, 1990; Shute, 1990; Corbet & Koedinger, 1997
Intelligent Tutors (ITS) A brief history Ideal ITS: What student knows + pedagogical strategy = expert tutor Machine learning: what worked, and when it worked, and improve over time
Intelligent Tutors (ITS) A brief history Examples: Research ITS ITS At-Scale Support student s thinking process (VanLehn et al., 2005; Anderson et al., 1995) SQL- Tutor (Mitrovic & Ohlsson, 1999) Talk with students in NLP (Nye, Graesser, & Hu, 2014) ALEKS (Craig et al., 2013) Recognize and respond to differences in student emotion (D Mello et al., 2010; Arroyo et al., 2014) Cognitive Tutor (Pane et al., 2014) ASSISTments (Koedinger, McLaughlin, & Heffernan, 2010) Simulated students that enable human students to learn by teaching (Leelawong & Biswas, 2008; Matsuda et al., 2010)
Stupid tutoring systems, intelligent humans Ryan S Baker, 2016
Intelligent tutor personalizes development Day 3 Day 4 Day 2 Day 1 Email Tip 1x2 Email Tip 1x3 Email Tip1x Email Tip 1 Text feedback Learner feedback Text feedback Learner feedback Text feedback Learner feedback Text feedback Learner feedback
Is it that simple? Faculty have different incoming skills Faculty have different coaching needs Clinical schedules differ Learners are on different schedules than faculty Ideally, faculty improve towards mastery
Multiple variables Day 3 Email Tip 12x Day 4 Email Tip 13x Day 2 Email Tip 1x Day 1 Email Tip 1 Day 2 Email Tip 2x Day 3 Email Tip 22x Day 1 Email Tip 1 Day 5 Email Tip 14x Day 4 Email Tip 13x Day 3 Email Tip 12x
Is it that simple? Incoming Data & Content Data Collected Message opening (yes/no) Message opening (time) Faculty ratings of content (text) Faculty self-assessment of performance (text) Student ratings of faculty (text) Student ratings of faculty Faculty schedule Learner schedule Content messages by topic Content messages by expertise
Is it working? (Pilot data) Day 3 Day 4 Day 2 Day 1 Email Tip 3 Email Tip 4 Email Tip 2 Email Tip 1 76% avg opening rate 48% avg opening rate 21% avg opening rate 80% avg opening rate
Is it working? (Pilot Data) Day 3 Email Tip 12x Day 4 Email Tip 13x Day 2 Email Tip 1x Day 1 Email Tip 1 Day 2 Email Tip 2x x Day 3 Email Tip 22x x Day 1 Email Tip 1 x Day 5 Email Tip 14x x Day 4 Email Tip 13x Day 3 Email Tip 12x
Systems Considerations Email goes to Junk Schedule is complicated & often unclear Not all cell numbers provided Leadership support & encourage faculty
Case Study Your Turn! How would you facilitate faculty development at CU SOM with an intelligent tutor?
Take Home Messages Busy faculty are increasingly not attending traditional faculty development sessions Iterative digital coaching pilot was successful Intelligent tutors + machine learning might coach and learn to keep coaching towards expertise
Thank You! Janet Corral Janet.corral@ucdenver.edu
