appendix-b-akron analytics presentationmastery analytics satisfaction import evaluation suite...

Academics/Analytics Suite Colleen McConeghy,

Director of Academic Solutions Consulting

-teaching experience at Colorado State and University of Northern Colorado

Pearson LearningStudio l ©2010 2

Assessment and Analytics Suite Pearson Learning Studio

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Assessment and Analytics Suite

Trending and Analytics

Program Assessment

Portfolios

Mastery Analytics

Satisfaction Import

Evaluation Suite

Conditional Release


Enterprise Reporting Pearson LearningStudio

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Enterprise Reporting Monitor and Act on Key Performance Indicators

Analytics tool, powered by IBM Cognos, which enables institutions to: •  Identify “at risk” students based on user activity or grades • Determine benchmarks for improved retention and student success •  Implement strategic decisions, based on benchmarks • Measure the effectiveness of those changes over time


Enterprise Reporting

The Pearson LearningStudio Data Warehouse offered by the Academic Analytics Suite includes:

Enrollments

School, Department, Course and Individual Students

Grades

Detailed Grade Information

User Activity

Activity Minutes and Submission Counts

Learning Outcomes

Student performance on course tasks/objectives

User Data

Demographic Information and Extended User Properties


Enterprise Reporting Lifecycle


Reporting Lifecycle

Identify Trends •  Completion Rates

•  Top 5% vs. Bottom 5%


Reporting Lifecycle

Set Benchmarks •  Activity and Participation frequency


Data Dashboard

At-Risk Activity Dashboard • Displays at-a-glance breakdown of student and instructor participation. • Displays user contact information, for immediate communication/ intervention


Reporting Lifecycle

Monitor Performance • At-Risk and Exception Reporting


Product Export Capabilities Report Export to Excel, PDF, XML or HTML


Standard Reports

Standard Reports • Student Activity •  Instructor Activity • Grades

• Enrollments

• Completion Rates


Reporting Lifecycle

Evaluate •  Did things improve?


Create reports on the fly •  Drag and drop fields to create custom queries.

•  Select from hundreds of fields in multiple categories.

Design personalized layouts to meet specific needs

Product Features: Custom Reports

• the use of data and models to predict student progress and performance, and the ability to act on that information • Brown, M. (2011). Learning Analytics: The Coming Third Wave. EDUCAUSE Learning Initiative Brief, April 2011, 1-4. doi:ELIB1101

Learning Analy,cs Defined

Topics:

§ Persistence

§ Completion and Performance

§ Opportunities

Deeper Dive: Interactivity § Course Design

§  Instructor Influence

§ Student Influence

Drouin, M. (2008, Fall). The rela(onship between students’ perceived sense of community and sa(sfac(on, achievement, and reten(on in an online course. Quarterly Review of Distance Educa6on, 9(3), 267-‐284.

Graff, M. (2006). The importance of online community in student academic performance. The Electronic Journal of e-‐Learning, 4(2), 127-‐32.

Moore, M. G. (1993). Theory of transac(onal distance. In D. Keegan (Ed.), Theore6cal principles of distance educa6on (pp. 22-‐38). New York: Routledge.

Rovai, A. P.(2002). Building a sense of community at a distance. Interna6onal Review of Research in Open and Distance Learning, 3(1), 1-‐16.

Rovai, A., & Wigh,ng, M. (2005). Feelings of aliena(on and community among higher educa(on students in a virtual classroom. Internet and Higher Educa6on, 8, 97-‐110.

DuJon, J., DuJon, M., and Perry, J. Do online students perform as well as lecture students. Journal of Engineering Educa6on. 90(1): 131-‐141, 2001.

Glahn, R. and Gen, R. Progenies in educa(on: The evolu(on of internet teaching. Community College Journal of Research and Prac6ce. 26: 777-‐785, 2002.

Achilles, C.M., Finn, J.D., and Bain, H.P. Using class size to reduce the equity gap. Educa6onal Leadership. 55: 40-‐43. 1998.

Lou, Y., Abrami, P.C., and Spence, J.C. Within-‐class grouping. Review of Educa6onal Research. 66: 423-‐458. 1996.

Abrami, P.C., Lou, Y., Chambers, B., Poulsen, C., and Spence, J.C. Why should we group students within-‐class for learning? Educa6onal Research and Evalua6on. 2: 158-‐179. 2000.

Deeper Dive: Interactivity § Course Design

§  Instructor Influence

§ Student Influence

Unit Number

Size/ Color = Average # of responses per user, per course type, per unit.

Range : 0 to 9

Discussion density by course type. Analytics

Interactivity

Course Type

• 10 Students • 15 Drops • Average Grade: 43%

Section 1

• 18 students • 7 drops • Average Grade: 72%

Section 2

Target Sections Analytics

Interactivity

Thread Interaction, visually.

Section 1

• 10 Students • 15 Drops

Section 2

• 18 students • 7 drops

Analytics

Interactivity

Pearson LearningStudio l 2010 16

Summary


Learning Outcome Manager Pearson LearningStudio

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Learning Outcome Manager Build a Culture of Assessment and Enhance Accreditation Management Build a Culture of Assessment and Enhance Accreditation Management Pearson LearningStudio solution allows institutions to:

•  Link course content items to tasks/objectives •  Assess student progress against course tasks/objectives

using rubrics •  Identify of “at risk” students early •  Analyze to improve curriculum and instruction •  Roll-up data to the Program and/or Institutional level


What’s Measurable?

Mission Statement

Goals

Outcomes & Standards

Course

Objectives

or Tasks

Institutional Level

Program Level

Course Level

Course Activities/Assessment


Learning Outcome Manager


Define Assessment Structure


Assess Student


In Course Reporting


Outcome Analytics: Raw Data


Outcome Analytics: Graphic Representation


Learning Outcome Achievement

LO6 Student Mastery by Course

Avg. = 55%


Learning Outcome Analyses

Student Mastery by Learning Outcome

LO6 LO7


Summary

appendix-b-akron analytics presentationmastery analytics satisfaction import evaluation suite...

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