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The Indicators Project Identifying effective learning: adoption, activity, grades and external factors Colin Beer David Jones Ken Clark http://tr.im/FvRQ Twitter: #eair

The indicators project - ASCILITE version

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A reworked version of presentation for ASCILITE'09 paper (http://tr.im/FD7a).

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Page 1: The indicators project - ASCILITE version

The Indicators ProjectIdentifying effective learning: adoption, activity, grades and external factors

Colin BeerDavid JonesKen Clark

http://tr.im/FvRQ

Twitter: #eair

Page 2: The indicators project - ASCILITE version

http://www.flickr.com/photos/anders-vindegg/3408838186/

Surprising patterns in CQU LMS data

Apparent benefit in LMS usage analysis that is

Different from established patterns

Due to limitations in current work?

Cross-LMS

Cross-institutional

Longitudinal

Want to play?

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Overview

http://www.flickr.com/photos/atbaker/1577665185/

Context

Level of feature adoption

Link between participation & grades

Reflection and Future work

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Overview

http://www.flickr.com/photos/atbaker/1577665185/

Context

Level of feature adoption

Link between participation & grades

Reflection and Future work

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http://www.flickr.com/photos/dnorman/251646154/

The data that we, educators gatherand utilize is all but garbage.(Wiley,

2009)

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http://www.flickr.com/photos/mikebaird/2087879492/

(Dawson, 2009; Dawson et al, 2009)

(Bakharia et al, 2009)

(Malikowski, 2008; Malikowski, et al, 2007;Malikowski et al, 2006)

(Campbell et al, 2007)

(Griffiths, 2007)

(Dawson, 2006)

(Black et al, 2008)

(Dawson et al, 2008)

(Heathcoate et al, 2005)

(Hornik et al, 2008)

(Romero et al, 2007)

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http://www.flickr.com/photos/mwichary/2188958154/

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LMS reporting toolsOnly what is in the

LMS

Limited views

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http://www.flickr.com/photos/mwichary/2188958154/

Some surveysHuman

recollectionLMS reporting toolsOnly what is in the

LMS

Limited views

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http://www.flickr.com/photos/mwichary/2188958154/

Some surveysHuman

recollection

Manual checksTime-

consuming

Error prone

LMS reporting toolsOnly what is in the

LMS

Limited views

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http://www.flickr.com/photos/mwichary/2188958154/

Some surveysHuman

recollection

Limited longitudinal data

Manual checksTime-

consuming

Error prone

LMS reporting toolsOnly what is in the

LMS

Limited views

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http://www.flickr.com/photos/mwichary/2188958154/

Some surveysHuman

recollection

Limited longitudinal data

Limited cross-LMS comparisons

Manual checksTime-

consuming

Error prone

LMS reporting toolsOnly what is in the

LMS

Limited views

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http://www.flickr.com/photos/mwichary/2188958154/

Some surveysHuman

recollection

Limited longitudinal data

Limited cross-LMS comparisons

Limited cross-institutional comparisons

Manual checksTime-

consuming

Error prone

LMS reporting toolsOnly what is in the

LMS

Limited views

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“LMS” situation

Students

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Webfuse 1997-2009

Pre-2010 “LMS”

WebCT 1999-2004

Blackboard 2004-2009

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Webfuse 1997-2009

Post-2010 “LMS”

Blackboard 2004-2009

Moodle….

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Students - 2009

CQ on-campus

4436

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Students - 2009

CQ on-campus

Distance education

4436

8444

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Students - 2009

CQ on-campus

Distance education

AIC on-campus

4436

8444 796

2

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The data that we, educators gatherand utilize is all but garbage.(Wiley,

2009)

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http://indicatorsproject.wordpress.com

Enabling comparisons of LMS usageacross institutions, platforms and

time

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http://www.flickr.com/photos/david_jones/36379215/

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Overview

http://www.flickr.com/photos/atbaker/1577665185/

Context

Level of feature adoption

Link between participation & grades

Reflection and Future work

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http://www.flickr.com/photos/chokingsun/3495110670/

There are more similarities than differences among LMS (Black et al,

2007)

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TransmittingContent

CreatingClass

Interactions

ComputerBased

Instruction

Move to features in unexplored categories, until each category is considered for different learning needs

Level 1: Most used Level 2: Moderately used Level 3: Rarely used

• Content/files• Announcements• Gradebook

More than 50% of courses More than 20% of courses (much) less than 20% of courses

EvaluatingStudents• Quizzes• Drop box

Evaluatingcourse and instructors

• Surveys• Chat• Discussion forum• Email

• Some quizzes• Adaptive release

(Malikowski et al, 2007)

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http://www.flickr.com/photos/chokingsun/3495110670/

What we did

CQU

Wf BbLMS logs

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http://www.flickr.com/photos/chokingsun/3495110670/

CQU

Wf BbLMS logs

LMS Independent

Data

What we did

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http://www.flickr.com/photos/chokingsun/3495110670/

CQU

Wf BbLMS logs

LMS Independent

Data

What we did

(Malikowski et al, 2007)

% of courses adopting “Malikowski features”

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Year Blackboard Webfuse2005 777 3042006 810 2962007 874 2522008 945 2252009 868 174

# course sites per “LMS”

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Malikowskitop & bottomrange

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Blackboard

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http://www.flickr.com/photos/chokingsun/3495110670/

Webfuse

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http://www.flickr.com/photos/chokingsun/3495110670/

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http://www.flickr.com/photos/chokingsun/3495110670/

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http://www.flickr.com/photos/chokingsun/3495110670/

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The particular trajectory of emergence is not wholly

determined either by the intentions of the human actors or by the material properties of technology, but rather by the interplay of the two(Jones,

1999)

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http://www.flickr.com/photos/nedrichards/3234490934/

Future Work

Will Moodle be different?

Why?

What about other institutions?

Are Malikowski ranges out of date?

When is a feature adopted?

How

…….

Page 39: The indicators project - ASCILITE version

Overview

http://www.flickr.com/photos/atbaker/1577665185/

Context

Level of feature adoption

Link between participation & grades

Reflection and Future work

Page 40: The indicators project - ASCILITE version

http://www.flickr.com/photos/wolfgangstaudt/2279651479/

The results indicated a significant difference between low and high performing students in terms of the quantity of online session times attended during the course (Dawson et al,

2008)

Discussion forum activity has been demonstrated to be a sound indicator of future student academic performance. (Dawson, 2009; Morris et al,

2005)

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What about

Different student types?

Different levels of staff interaction?Different staff academic background?

(Fresen, 2007)

CQU context

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CQU

Wf BbLMS logs

LMS Independent

Data

What we did

(Malikowski et al, 2007)

Feature usage

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http://www.flickr.com/photos/chokingsun/3495110670/

CQU

Wf BbLMS logs

LMS Independent

Data

What we did

(Malikowski et al, 2007)

Feature usage

Student records

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http://www.flickr.com/photos/chokingsun/3495110670/

CQU

Wf BbLMS logs

LMS Independent

Data

What we did

(Malikowski et al, 2007)

Feature usage

Participation, grades,external factors

Student records

Page 45: The indicators project - ASCILITE version

Participation

Hits

Discussion forum

Entire course siteJust course forum

# replies# posts

http://www.flickr.com/photos/adactio/1259245482/

N = 510,158

Student/courses

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http://www.flickr.com/photos/david_jones/36379215/

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Mixed design ANOVA

All reported effects are significant

Significant interaction effectbetween grades and participation

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Hits on course site

Hits on course forum

Distance education students

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http://www.flickr.com/photos/wolfgangstaudt/2279651479/

CQ on-campus students

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AIC students

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AIC

CQ

FLEX

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Distance education students

# of replies

# of posts

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CQ on-campus students

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http://www.flickr.com/photos/wolfgangstaudt/2279651479/

AIC students

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http://www.flickr.com/photos/wolfgangstaudt/2279651479/

The results indicated a significant difference between low and high performing students in terms of the quantity of online session times attended during the course (Dawson et al,

2008)

Discussion forum activity has been demonstrated to be a sound indicator of future student academic performance. (Dawson, 2009; Morris et al,

2005)

Page 56: The indicators project - ASCILITE version

http://www.flickr.com/photos/wadem/2730257498/

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What about

Different student types?

Different levels of staff interaction?Different staff academic background?

(Fresen, 2007)

CQU context

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http://www.flickr.com/photos/orinrobertjohn/3321067724/

Course groups based on staff hits

Group Courses Student-courses

Students/course

Definition

High 678(17%)

156,770 231 x>3000 hits

Medium 980(25%)

143,314 146 3000>x>1000

Low 1474(37%)

102,214 69 1000>x>100

Super low 849(21%)

68,354 80 100>x>0

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Hits

High staff participation courses

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Replies & posts

High staff participation courses

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Hits

Super low staff participation courses

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Replies & posts

Super low staff participation courses

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High

Posts comparisons

Superlow

Page 64: The indicators project - ASCILITE version

http://www.flickr.com/photos/wolfgangstaudt/2279651479/

The results indicated a significant difference between low and high performing students in terms of the quantity of online session times attended during the course (Dawson et al,

2008)

Discussion forum activity has been demonstrated to be a sound indicator of future student academic performance. (Dawson, 2009; Morris et al,

2005)

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http://www.flickr.com/photos/wadem/2730257498/

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Future Work

- Course size

Examination of super-low

Networks of interaction (Bakharia et al, 2009; Dawson,

2009)

Forum content

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What about

Different student types?

Different levels of staff interaction?Different staff academic background?

(Fresen, 2007)

CQU context

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Course offerings with

Staff with grad certs in L&TSignificant input from instructional designerStaff who have received teaching awards

Staff teaching education coursesNone of the above

n=167

n=4

n=196

n=3288

N=362

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None of the above

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Grad cert in L&T

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Teaching awards

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Education discipline

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Instructional design

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Overview

http://www.flickr.com/photos/atbaker/1577665185/

Context

Level of feature adoption

Link between participation & grades

Refleection and Future work

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http://www.flickr.com/photos/mikebaird/2985066755/

Some interesting patterns

Useful idea

Needs more work

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Institution A

LMS 1 LMS 2

Institution B

LMS

Institution C

LMS

LMS Independent

Data

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

T&L Practice Research

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Institution A

LMS 1 LMS 2

Institution B

LMS

Institution C

LMS

LMS Independent

Data

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

T&L Practice Research

Enabling

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Institution A

LMS 1 LMS 2

Institution B

LMS

Institution C

LMS

LMS Independent

Data

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

T&L Practice Research

Enabling

What

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http://www.flickr.com/photos/wolfgangstaudt/2279651479/

Avg course hits

DE under grad students

By age

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Institution A

LMS 1 LMS 2

Institution B

LMS

Institution C

LMS

LMS Independent

Data

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

T&L Practice Research

Enabling

What

Why

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Institution A

LMS 1 LMS 2

Institution B

LMS

Institution C

LMS

LMS Independent

Data

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

Useful Data/Informat

ion

T&L Practice Research

Enabling

What

WhyHow

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http://tr.im/FvRQ

http://indicatorsproject.wordpress.com

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Bakharia, A., E. Heathcote, et al. (2009). Social networks adapting pedagogical practice: SNAPP. Same places, different spaces. Proceedings ascilite Auckland 2009. Auckland: 49-51.

Black, E. W., K. Dawson, et al. (2008). "Data for free: Using LMS activity logs to measure community in online courses." Internet and Higher Education 11(11): 65-70.

Black, E., D. Beck, et al. (2007). "The other side of the LMS: Considering implementation and use in the adoption of an LMS in online and blended learning environments." Tech Trends 51(2): 35-39.

Campbell, J., P. DeBlois, et al. (2007). "Academic analytics: A new tool for a new era." EDCAUSE Review 42(4): 40-42.

Coates, H., R. James, et al. (2005). "A Critical Examination of the Effects of Learning Management Systems on University Teaching and Learning." Tertiary Education and Management 11(1): 19-36.

Dawson, S. (2009). "'Seeing' the learning community: An exploration of the development of a resource for monitoring online student networking." British Journal of Educational Technology: in press.

Dawson, S., L. Macfadyen, et al. (2009). Learning or performance: Predicting drivers of student motivation. Same places, different spaces. Proceedings ascilite Auckland 2009, Auckland.

Dawson, S., E. McWilliam, et al. (2008). Teaching smarter: How mining ICT data can inform and improve learning and teaching practice. Hello! Where are you in the landscape of educational technology? Proceedings ascilite Melbourne 2008. Melbourne: 221-230

Dawson, S. (2006). "Online forum discussion interactions as an indicator of student community." Australian Journal of Educational Technology 22(4): 495-510.

Fresen, J. (2007). "A taxonomy of factors to promote quality web-supported learning." International Journal on E-Learning 6(3): 351-362.

Griffiths, M. E. (2007). Patterns of user acitivity in the Blackboard course management system across all courses in the 2004-2005 academic year at Brigham Young University. Department of Instructional Psychology and Technology., Brigham Young. Master of Science: 117.

Heathcoate, L. and S. Dawson (2005). "Data Mining for Evaluation, Benchmarking and Reflective Practice in a LMS." E-Learn 2005: World conference on E-Learning in corporate, government, healthcare and higher education.

Hornik, S., C. S. Saunders, et al. (2008). "The Impact of Paradigm Development and Course Level on Performance in Technology-Mediated Learning Environments." Informing Science. The international journal of an emerging transdiscipline 11(11).

Jones, M. (1999). Information systems and the double mangle: Steering a course between the scylla of embedded structure and the charybdis of strong symmetry. Information Systems: Current Issues and Future Chalenges. T. Larsen, L. Levine and J. DeGross. Laxenburg, Austria, IFIP: 287-302.

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Malikowski, S., M. Thompson, et al. (2007). "A model for research into course management systems: bridging technology and learning theory." Journal of Educational Computing Research 36(2): 149-173.

Malikowski, S., M. Thompson, et al. (2007). "A model for research into course management systems: bridging technology and learning theory." Journal of Educational Computing Research 36(2): 149-173.

Malikowski, S., M. Thompson, et al. (2006). "External factors associated with adopting a CMS in resident college courses." Internet and Higher Education 9(3): 163-174.

Morris, L., C. Finnega, et al. (2005). "Tracking student behavior, persistence, and achievement in online courses." Internet and Higher Education 8(3): 221-231.

Romero, C. b., S. n. Ventura, et al. (2007). "Data mining in course management systems: Moodle case study and tutorial." ScienceDirect Computers and Education (51 (2008)): 368-384.

Salmon, G. (2005). "Flying not flapping: a strategic framework for e-learning and pedagogical innovation in higher education institutions." ALT-J, Research in Learning Technology 13(3): 201-218.

Wiley, D. (2009). "The LHC and education." Retrieved 29 November, 2009, from http://opencontent.org/blog/archives/1098.