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Redesign characteristics:

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It’s hard to do good pedagogy, tech helps bring that to scale.

Six models:
- supplemental (change but not too much)
- Replacement (flipped)
- Emporium – Move all classes into a lab setting, students working with instructional software.
- Fully online -
- Buffet – (Ohio State, stopakong them take a full meal, give them a choice) students mix & match
- Linked workshop (developmental courses)

Examples:

  • Fairfield University (biology) slide
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  • First year Spanish
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  • Math Emporium, Virginia Tech; replicated at other schools, cost reduction less (30% not 70%, red cups)
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Looking for Pedagogy in Blended Course Design (there are some additional resources here)
Patricia McGee, Univ of Texas & San Antonio
Digital Learning Design Program,UT
Educause Learning Initiative (ELI) webinar

Agenda:

  • Introduction
  • Best Practices for Design
  • Pedagogical Practices
  • Preparing Students

Meta-analysis: Best practices – published literature about course design for blended learning; encoded repeated items.  Found the same language used, even when sources were not references.  Journal of Asynchronous Learning Networks

Pedagogical Reports – looking at the literature about what students (mostly) and faculty (not much done) do in the blended env’t.

Design Based Research – Members of the team gather data & make changes to the program as it unfolds. (possible connection to Summer Academy); Blended & Online Learners

Content Analysis of Blended Models – found 27 different models so far.

Hybrid vs Blended learning definitions: for her – hybrid is like a car, switching between 2 modes, not much integration; blended (for her) means that there is a deliberate connection between them.

Common definition of blended – combined elements of f2f & online courses; provides substantial portion (30-70%) of content online, typically relying on discussions within a planned & pedagogically driven design.  Her definition of pedagogy is to lead the learner. (discussion of appeal & challenges of blended learning)

Workforce blended/hybrid models – more varied than higher ed.  Higher Ed models:

Hyflex model (Beatty); http://www.drbrianbeatty.com/wordpress/; one course, students choose to meet partially online or not (options A – attend all classes in person (excused from some online activities, e.g. discussions), B – commit to come to 1 f2f/month, C – all online) stick with that model for the semester.  All students participate in all the activities/assignments they need to do complete the course.  Graduate level.  Fewer of their students do the fully online, they expect everything to be asynchronous online & that’s how she does it.  Difference in performance?  Classroom students tend to procrastinate, blended ones seem to drop out.   She has them sign up on a Google doc (no contract), she includes instructions for each choice.  Limited to 30 students.  Univ of Toronto (Simone Laughton) – using this with a class of over 700 students with no significant difference in outcomes.

Multimodal model – Picciano, 2009; Components: Content, Social/emotional, Dialectic/questioning, Synthesis/evaluation, Collaboration/student generated content, Reflection.  Blending with purpose: The multimodal model. Journal of the Research Center for Educational Technology, 5(1). Kent, OH: Kent State University

Community of Inquiry model – Vaughan & Garrison, 2009; Integrating Social, Cognitive, Teaching Presences; http://communitiesofinquiry.com/blhighered

General resource: International Society for Technology in Education (ISTE): https://www.iste.org/

Best Practices: Wicked Problems – no specific alternative solution, difficult to tell when it is solved, complex in nature, may have political, professional dimensions, no right/wrong (more like better/worse solution)

Course Re-Design – avoids building a course & a half, typically requires three to six months (200-350 total hours), objectives written from the student perspective are best, f2f meetings should require active participation.  One summary of time expectations: http://digitalcampus.umn.edu/faculty/plan/time-cost.html

Typically there is no direct translation from one env’t to another (i.e. classroom conversation is not the same as online discussion), need to think about why we’re doing each thing.  Alignment – nice table: objective|activity|location|assignment|assessment.  Peer Review – need to have someone else look at what you’re doing (e.g. Quality Matters is one approach)

Bergtrom, G (2011). Content vs learning: An old dichotomy in science courses, Journal of Asychronous Learning Networks, 15(1). 33-44.  http://sloanconsortium.org/publications/jaln_main

Make a Blend: Online tutorial > online discussion > classroom project > peer critique > online test/authentic assessments

Varied approaches are needed: formal (teacher-directed) & informal (learner-centered)

Course Implementation – communicate expectations clearly (Syllabus handout), focus on learner accountability, periodically evaluate course

Pedagogical Practices (in contrast to the best practices we just reviewed) – focus on activity to determine the blend, f2f is the priority, pedagogical template vs routine activity.  Dominant online activities: Discussion, content presentations, assignment/assessement, group work.

3 ways that people organize active learning:

  1. Product – focus on practice through isolated or progressive activities; creating parts of the whole
  2. Process – assignments & activities support that document learner’s mastery
  3. Project – step by step with benchmarks (doesn’t have to end in a product), demonstrates mastery

Layering: Structural Dimensions (where learning occurs & how it is connected) > Dynamical Dimension (shift from technology to learning that occurs over time)

Case-based layering (Glazer, 2010), Game-based methods (Shang, Jong, Lee & Lee, 2008, p. 34), Online processing, Streamlining (Fuketh, 2009)  (Activity during break – Blended Cases handout (we don’t have the handout…))

Smarter Measures: http://www.smartermeasure.com/ (fee based, web-based, 124-item assessment which measures a learner’s readiness for succeeding in an online and/or technology rich learning program)

How to prepare your students for online learning, how to know if they are prepared?

Highlights:

  • Monday: Facilitated a half day iPad session that was a lot of fun
  • Tuesday: Facilitated a “Birds of a Feather” lunch table on Learning Outcomes Assessment. Had a great conversation, learned that our colleague from CT community college was the only school at the table with anything like systematic or comprehensive defined learning outcomes & assessment criteria across the curriculum. Enjoyed a session on a three nation marketing course, session on the future of student computer labs from UNH, and an unconference session.
  • Wednesday: Great faculty story about shifting to an active learning stance from BU, WordPress ePortfolio from Granite State College (poster), What the MOOC?

Effective Practices in Teaching with Technology at Tufts

1. Trunk Forums to Enhance Reflective Learning:
Jonathan Garlick, D.D.S., Ph.D., Professor, Oral Pathology, Director, Division of Tissue Engineering and Cancer Biology, School of Dental Medicine – using Trunk forums to help students go deeper with their learning & fostering reflective learning. Pyramid integrating: foundational science literacy, “real life” implications, broader impacts, reflective interpersonal perspective > Outcomes (Human potential, Human values, Informed citizenship).  How: weekly discussion topic, everyone does the same reflective reading, pre-class conversation, bring it in class.

2. The Development of Video-Based Clinical Ultrasound Teaching Tools for Veterinary Students:
James Sutherland-Smith, BVSc., DACVR, Assistant Professor, Clinical Sciences, Cummings School of Veterinary Medicine – great use of videos & illustration to improve instruction by creating dual-view videos (ultrasound image & external camera, with voiceover).  Lovely use of Prezi for the presentation.  Proving the educational value would be future opportunity, but student feedback was very positive.

3. Implementing Technologies for Blended Learning:
Libby Bradshaw, DD, MS, Academic Director, Master of Science/Certificate Program, Pain Research, Education and Policy Program (PREP), Public Health and Professional Programs, Public Health and Community Medicine, School of Medicine – Program level presentation – how they have moved forward with their blend.  Half of overall course delivered face to face, half online, used Echo Capture, Jabber video, Trunk & TUSK.

 

Feb 25, 2013:

As usual, my “real” life has dashed my high hopes for my own active participation in this MOOC.  HOWEVER, I always get a lot out of just seeing how the course is designed & delivered & in addition, in this case, George generously referred us to an intro Stats course (from a competing MOOC, EdX).  The referral came before I had even realized that I needed the additional background, which I consider teaching genius, and it turns out to have been spot on, so I’ve been devoting my leisure time to that course instead, hoping that the stats background will make my eventual Learning Analytics work both more enjoyable & more productive. Because of this, I’m eliminating the separate page on my blog for this course & putting this into a post along with the rest of my explorations.

I have also noticed that I’m starting to have trouble remembering where each mooc is – EdX, Coursera, Canvass, oh my!  A student portal & portfolio to aggregate & display all mooc accomplishments would be a great tool for someone to develop (or to explain good ways to create with existing tools.)

Feb 11, 2013:

Well, this is a big step…the “Learning Analytics and Knowledge 2013” MOOC looks so promising from today (first day!) that I’ve decided to create a WHOLE PAGE for it!

What I like so far is:

  • Nice video intro from George, which I can listen to in less than 5 minutes while
  • Browsing the course website: https://learn.canvas.net/courses/33/wiki/front-page
  • Course site includes great intro materials, including:
    • an overview of the course that I can quickly understand
    • course outline
    • expectation setting: total time PLUS explanation of centralized & decentralized elements.
    • clear info & links to related elements (twitter hashtag (#LAK13) & Diigo group, a few others), quite a variety, but not too many.

Overall, pretty exciting start – I got my first 10 mins worth & hope I can come back to it soon for more!

Great talk this morning at MIT:

Using Big Data to discover tacit knowledge & improve learning

Ken Koedinger, Prof of Human-computer interaction & psychology, Carnegie Mellon University
CMU Director of LearnLab, Pittsburgh Science of Learning Center

http://www.learnlab.org

Recent Powerpoint (from a similar talk) with good notes:

“Why is it that science & tech have not improved education as they have medicine and transportation? A root cause is that we, the general public, educators, policy makers, do not fully appreciate the complexity of learning and instruction.   Learning is really much more complex than our conscious experience of it would suggest.

Our over-estimated sense that we understand our own learning leads us astray in making educational decisions — it yields a tendency toward the quick fix or one size fits all solution. … The good news is that there is so much S&T can do to better understand learning and to greatly improve instruction.  We first need to accept that we do not know what we know!”

Introduced by Lori Breslow, Director, Teaching and Learning Laboratory at MIT.  Ken’s lecture was recommended to her as best lectures about online learning, offered as part of MIT DUET Seminar Series.

—–

Ken’s talk:

Full notes in PDF format, including some pretty bad photos (but taking notes on the iPad & inserting photos into them is so fun!).

Most of what we know is tacit > learning based on intuition is flawed (great argument overview slide, see if I can get a copy).

Chick sexing – experts can sex chicks, but can’t explain how & it takes awhile to learn (Beckmann & Shiffar, Sexing Day Old Chicks), half page instruction improved learning curve.  Eg we know English but we don’t know what we know.  Experts can describe less than 30% of what they know > major design implications.  Cognitive Task Analysis (lee 2004 meta analysis) improves instruction

Teachers don’t know what they know (eg story problem, word problem, equation) – math teachers & us think story is hardest for students, but equation is (for beginning algebra students). They have trouble with the symbolic language of the equation.  >> Expert Blind Spot – not their fault, but problematic for instructional design.  (Difficulty factors assessment)

From textbook model to inductive support model.  Algebra Cognitive Tutor: interactive support for learning by doing – within activity: authentic problems, feedback within complex solutions, challenging questions, personalized instruction (specific to their need at the moment), between activities: progress & individualized reference to next problem.
Model tracing (like AI plan recrognition); represent all correct paths, everything else is an error.  Knowledge tracing: assess knowledge growth, driving activity selection & placing.  Use the data you are collection to measure effectiveness of the model.  Some verifiable results (some null too), 600k students using it per year, 80 mins per week.  (Using in Algebra, Chemistry, English, Games)
EdTech + wide use = Research in practice
Studies run for a couple weeks, designed to test a specific item/ change.
Interaction data is surprisingly revealing (Worcester Polytechnic)
  • accurate prediction of mcas score
  • detect student work ethic, engagement
  • discover better models of what’s hard to learn

Analysis of Open Learning Initiative data set from stats course.  In algebra, the real challenge is learning to model the problem, not solving equations.

“Sciences of the Artificial”.The task, not the cognitive process drives the learning. Inherent difficulty in the task is not obvious, so instruction is misguided.

Need to design the instruction to get at the underlying tacit knowledge. Learning Factors Analysis

  • Traditional College Course=>100hours, ~3% learning gain
  • Adaptive Data-Driven Course=<50 hours, ~18% learning gain

Experts in the domain/field need to agree in order to make this all possible.

 

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