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Proposal to Use Massive Online Tutoring Systems (MOTS) to Outsource Math Learning Support

Proposal to Use Massive Online Tutoring Systems (MOTS) to Outsource Math Learning Support. * Presentation by Paul Stephen Prueitt, PhD January 17, 2013. The Basic Statistic. Uri Treisman reports * that only 33% of students entering mathematics learning support exit learning support.

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Proposal to Use Massive Online Tutoring Systems (MOTS) to Outsource Math Learning Support

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  1. Proposal to Use Massive Online Tutoring Systems (MOTS) to Outsource Math Learning Support * Presentation by Paul Stephen Prueitt, PhD January 17, 2013

  2. The Basic Statistic • Uri Treisman reports * that only 33% of students entering mathematics learning support exit learning support. * Figure from: Cullinane and Treisman, (2010), “Improving Developmental Mathematics Education in Community Colleges”

  3. Student Pathways in College Mathematics Class • An algorithm was developed by Prueitt (2013) that produces a graph based representation of all student advancement in all of the first two years of college, including learning support. • The input to produce this graph is a SQL query to the permanent student records in the Registrar’s Office • Using the graph, an analysis may be made regarding the successfulness of the department of mathematics, including learning support

  4. New Methods in Social Media • A consortium will run a MOTS (Massive Online Tutoring System) • If minimal outcome metrics are not meet, the department’s learning support program will be outsourced to the MOTS • Member colleges will contribute faculty release time • The MOTS will be low cost or no cost to students, and will not carry any credits. • Exiting the MOTS is determined by a two level, skill plus synthesis, adaptive assessment rubric • This rubric will use ALEKS knowledge-space technology and subjective evaluation of hand written exams by a professor.

  5. MOTS Assessment • Handwritten narrative type tests are using to evaluate the level of synthesis that each student is demonstrating • The ALEKS adaptive assessment software is used to provide exercise sets, and interactive and individualized instruction • Professors use a deep learning methodology involving a four step method and narrative compositions • Handwritten assessments can be photographed or smart pens used to decrease likely hood of false identity of assessments • Student based Peer-2-Peer social networks are encouraged • Video clips are produced from professor lectures • Students may compete for “best written” narrative tests, and these posted

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