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Learning and Teaching Algebra in a Connected Classroom. Douglas T. Owens 1 , Stephen J. Pape 2 , Karen E. Irving 1 , A. Louis Abrahamson 3 , Vehbi A. Sanalan 1 1 The Ohio State University 2 University of Florida 3 Better Education Foundation

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learning and teaching algebra in a connected classroom

Learning and Teaching Algebra in a Connected Classroom

Douglas T. Owens1, Stephen J. Pape2,

Karen E. Irving1, A. Louis Abrahamson3,

Vehbi A. Sanalan1

1 The Ohio State University

2 University of Florida3 Better Education Foundation

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305K050045 to The Ohio State University.  The opinions expressed are those of the authors and do not represent views of the U.S. Department of Education.

additional research team
Additional Research Team

Frank Demana,Co-PI,The Ohio State University

Christy Boscardin, Joan Herman, Hye Sook Shin,David Silver,UCLA, CRESST;

Clare Bell, & Melissa Shirley,OSU

Mike Kositzke,Project Program Coordinator, OSU

Ugur Baslanti,University of Florida

Sukru Kaya,The Scientific and Technological Research Council of Turkey

TI Navigator slides adapted from a presentation by Eileen Shihadeh, Texas Instruments

ccms project overview
CCMS Project Overview
  • Interdisciplinary professional development and research project
  • Algebra I and Physical Science
  • Classroom connectivity technology
  • Summer Institute – training
  • T3 conference follow-up
slide4

22-Mar-08

The TI-Navigator™ Connected Classroom

The TI-Navigator System allows the teacher to:

  • Create a collaborative learning environment
  • Engage in formative assessment by way of immediate feedback
  • Enhance classroom management of TI graphing technology
background of ccms study
Background of CCMS Study
  • Changing conception of mathematics competence (Kilpatrick, Swafford, & Findel, 2001)
  • Changing roles for teachers include
    • Thinking beyond skills-based conceptions
    • Setting norms for discourse
    • Using problem solving and inquiry to support knowledge construction
    • Using formative as well as summative assessment
    • Developing student self-regulated learning
theoretical framework
Theoretical Framework
  • Social-constructivist models of teaching and learning
  • Technology-assisted formative assessment
  • Classroom environments that foster self-regulated learning and mastery orientation
  • Classroom discourse processes
  • Classroom environment centeredness constructs
prior research roschelle penuel abrahamson 2004
Prior Research (Roschelle, Penuel, & Abrahamson, 2004)
  • Students:
    • Increased student engagement, understanding, and interactivity
    • Improved classroom discourse
    • Knowledge of classmates’ learning
  • Teachers:
    • Improved pre- and post- assessment of student learning
    • Increased awareness of student difficulties
    • Improved questioning
audience response systems in academic settings
Audience Response Systems in Academic Settings
  • Increases in …
    • Student attendance and participation(Burnstein & Lederman, 2001)
    • Student comprehension(Hake, 1998; Slain et al., 2004)
    • Student engagement(Dufresne et al. 1996)
    • Collaborative learning(Mazur, 1997)
    • Conceptual reasoning(Crouch & Mazur, 2001)
    • Student satisfaction(Judson & Sawada, 2002)
  • Technology-facilitated interactive engagement in ARS lecture classes is correlated with student conceptual gains (Judson & Sawada, 2002)
research questions
Research Questions
  • How does teachers’ use of connected classroom technology affect:
    • Student achievement in algebra 1?
    • Self-regulated learning strategic behavior?
    • Student views of mathematics?
research design
Research Design
  • Year 1 (2005-2006) – Algebra I
  • Randomized assignment to treatment and control/delayed treatment groups
  • Cross-over design – control group provided treatment in second year of participation
  • Mixed methodology
participants
Participants
  • Initial data – 127 Algebra I teachers and 1,761 students from 28 states
  • 81 (64%) teachers had complete data at the end of year 1 (Rx = 39; C = 42)
  • 1,128 students from 68 classrooms (84% of 81) with adequate data (n>9; Rx=617; 50.2% female; C=511; 56.8% female)
  • Initial and final samples were not different on teacher demographic characteristics
  • Final sample treatment and control differ: % free/reduced lunch and school location
teacher data collection
Teacher Data Collection
  • Demographic Information Form
  • Technology Use and Professional Development Survey
  • Teacher Instructional Practices and Beliefs Survey (TIPBS)
  • Implementation—Teacher Interviews (inter-rater reliability ranged from .80 to 1.00)
  • Level of content implementation
student measures
Student Measures
  • Algebra I pretest
  • Algebra I posttest
    • Total score
    • Visual, Mechanical, and Pure Symbolic subtests
  • Student Beliefs about Mathematics
  • Motivated Strategies for Learning Questionnaire (Pintrich, Smith, Garcia, & McKeachie, 1991)
measures algebra i
Measures – Algebra I
  • Algebra pretest – 30 item; 23 multiple choice, 3 short-answer, and 4 extended response
  • Algebra post-test – 30 items; 24 multiple choice, 1 short-answer, and 5 extended response
  • 11 items overlap between the pre- and post-tests
motivated strategies for learning questionnaire
Motivated Strategies for Learning Questionnaire
  • 6 Motivation subconstructs
    • Intrinsic/Extrinsic Goal Orientation; Task Value; Control of Learning Beliefs; Self-Efficacy; Test Anxiety
      • Alpha range = 0.67 to 0.92
  • 5 Learning Strategies subconstructs
    • Rehearsal; Elaboration; Organization; Critical Thinking; Metacognitive Self-Regulation
      • Alpha range = 0.73 to 0.80
  • 4 Resource Management Strategies Subconstructs
    • Time and Study Environment; Effort Regulation; Peer Learning; Help Seeking
      • Alpha range = 0.50 to 0.65
data analyses
Data Analyses
  • Cronbach’s alpha reliability estimates
  • IRT analysis conducted to ensure technical quality of Algebra pre- & post-test
  • Hierarchical Linear Modeling (HLM) to examine effect of treatment
    • Accounting for nested data
    • Pretest data included as covariate
    • Two-level models consisting of within-class (level 1) and between-class (level 2)
hlm analyses

~

HLM analyses
  • Level 1
  • Level 2
results
Results
  • Significant treatment effect (ES=0.30) after controlling for student pretest scores, teacher’s years of experience, teacher’s gender, and percent of free/reduced lunch
    • Students taught by treatment group teachers performed about 2 out of 37 points higher than control students
  • Level of teacher knowledge about students as a result of TI-Navigator use was positively related to with student performance (ES=0.36)
  • Frequency and level of technology implementation as well as level of instructionalchange with technology were not associated with the outcome
results22
Results
  • Teaching experience was positively associated with achievement
  • Percentage free/reduce lunch not associated with outcome
  • Students of female teachers performed higher than male teachers (ES = .41)
  • Level of content coverage (implementation) was not associated with student performance
  • None of the other teacher survey constructs were associated with student outcome
results23
Results
  • On visual dimension, after controlling for percentage of free/reduced lunch, positive association between outcome and …
    • Treatment status (ES = 0.34)
    • Frequency of technology use (ES = 0.32)
    • Level of teacher knowledge about students as a result of TI-Navigator use (ES = 0.40)
    • level of instructional change with technology(ES = 0.48)
  • For mechanical and pure symbolic questions, none of the variableswere positively associated with the outcome
results con t
Results (con’t)
  • Treatment positively affected student Self-efficacy/math performance expectations with (ES=0.16)
  • No differences for beliefs about mathematics, confidence, anxiety, or usefulness related to treatment
  • No differences for motivation, learning strategies, or resource management strategies related to treatment