ANALYSIS OF THE TECHNOLOGICAL, CONVERSATIONAL, AND PEDAGOGICAL INFLUENCES ON LEARNING QUADRATICS: A

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ANALYSIS OF THE TECHNOLOGICAL, CONVERSATIONAL,
AND PEDAGOGICAL INFLUENCES ON LEARNING
QUADRATICS A CASE STUDY

• Dissertation Proposal in
• Communication Information Sciences
• by
• Patricia J. Donohue

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Presentation

• Introduction
• Background
• Study Design Theoretical Foundation
• Theory Biases Point-of-view
• Contributions to Learning Theory
• Theories of Learning
• Technology in Teaching Learning
• Student Assessment Qualitative Research
• Study Description
• Methods of Analysis

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BackgroundStudy Design Theoretical Foundation

• Qualitative Research Case Study
• Field Study of two math classes
• Qualitative analysis base with quantitative
  analysis applications
• Mix of measures surveys, audio tapings,
  observations, grades and performance assessments
• Exploration of
• Individual and group methods for learning

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BackgroundStudy Design Theoretical Foundation

• Analysis based in Grounded Theory
• Inductive Process to derive theory from data,
  common themes, patterns
• Back Forth process
• Observations ?? Analysis
• Study foundations theoretical grounding in
• Theories about how we learn
• Collaborative learning
• Affective learning

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BackgroundTheoretical Bias Point-of-View

• Bias and POV
• Klein Myers, 1999 Principles for evaluating
  and conducting interpretive field studies
• Fundamental principle of the hermeneutic circle
• The idea of the hermeneutic circle suggests that
  we come to understand a complex whole from
  preconceptions about the meanings of its parts
  and their interrelationships (p. 71)
• Chi, 1997 Analyzing verbal data
• Every choice/selection of what to record, how to
  segment and code, what to categorize for analysis
  of verbal data is subject to theoretical bias and
  personal perception
• Study assumptions Belief in constructivist-
  inquiry-learning, qualitative analysis methods,
  affective learning, classrooms are poor learning
  environments, potential of technology,
  collaborative learning.

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Background Current Views

• Increased Demand for Assessment
• NCLB The No Child Left Behind Act (2002)
• Increasing assessment (students and schools)
• Decreasing content instruction
• Quantitative Testing vs. Qualitative Assessment
• Land Hannifin (2000) growing recognition of
  constructivist practices and child-centered
  teaching and learning
• Boylan (2005) NSFs 15 year investment shows
  very little has been done to assess the power of
  technology to individualize instruction and
  assessment

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Background Current Views

• Grand Challenges of Mind Brain (NSF, 2006)
• Research has begun to show glimpses into how we
  learn
• Report asks broad questions, i.e.
• How do we learn induction perception - to make
  leaps of understanding without sufficient
  information?
• How are mental representations such as percepts,
  concepts, and verbal ideas related to each other?
• How do different parts of the brain communicate
  to integrate information from different
  modalities?
• 6 Domains of research plasticity, conflict
  cooperation, spatial knowledge, time, language,
  causal understanding

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Background Current Views

• Focus of the field study explores 3 sources of
  influence on learning
• Technological
• Social
• Pedagogical

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Contributions to Learning TheoryTeaching
Learning Theory

• John Dewey
• Learning as Experience - personal construction of
  meaning in doing
• Behaviorism Pavlovs SR
• Constructivist Learning
• Building blocks of external world, activity,
  personal views, and abilities constructing
  meaning
• Changing role of learner recipient to active
  participant

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Contributions to Learning TheoryTeaching
Learning Theory

• Formal vs. Informal Approaches
• Hofstein Rosenfeld (1996) instructional
  strategies should tailor to the learners
  abilities and aptitudes
• Inquiry-Learning
• Informal learning approaches Benefits of
  strategies for engaging the learner in inquiry
• Socratic questioning
• Hands-on experience
• Student-centered/student-directed methods
• Development of HOTS

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Contributions to Learning TheoryTeaching
Learning Theory

• Collaborative Learning
• Dillenbourg, et al.(1996)
• As opposed to cooperative problem-solving where
  there is a division of labor among participants,
  collaboration involves a coordinated effort to
  solve problem.
• Goal to understand how one cognitive system is
  transformed by messages received from another
• shift of focus in collaborative learning theory
  from the individuals functioning in a group, to
  the group itself as a unit of analysis (p.189).
• Stahl, Koschmann, Suthers (2006)
• involves individuals as group members, but also
  involves phenomena like the negotiation and
  sharing of meanings including the construction
  and maintenance of shared conceptions of tasks
  that are accomplished interactively in group
  processes (p.3).

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Contributions to Learning TheoryTeaching
Learning Theory

• The Role of Affect in Learning
• Picard, et al. (2004) Need to close the gap
  between cognitive and affective learning
  theories when basic mechanisms of emotion are
  missing in the brain then intelligent functioning
  is hindered (p.253).
• Hudlicka (2004) In computer systems, the user
  is now the central component of system design and
  user needs drive both the nature of the user
  interface and the function allocation of tasks
  between the user and the machine (p.2).
• Nahl (1996, 2004, 2006) The role of affect in
  decision-making and attention defines what the
  learner sees and recognizes and how he/she reacts
  in the interaction and learning.

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Contributions to Learning TheoryTechnology in
Teaching Learning

• Technology as Tool
• Technical tools, e.g., graphing calculator,
  probes, GIS computation
• Computer-based instruction, AI, and ITSs
• Technical Assessment adaptive testing
• Emerging Technologies Their Use
• Online tools for teaching remotely, assessment,
  collaboration, etc.
• Psychophysiological sensors for assessment of
  learner state
• Tools for building communities of learning

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Contributions to Learning TheoryLearning
Assessment Qualitative Research

• Investigation of Qualitative Analysis Methods
• Garfinkel EM - a new approach to social
  analysis an exploration of social order
  exposing what is taken for granted in social
  interaction
• Goodman Heritage Evolution and contributions
  of CA analysis of the social interface between
  individual and group cognition integrating
  analyses of action knowledge
• Conversation Analysis of Collaborative Learning
• Koschmann CA techniques, e.g., LIs (learning
  issues), KASs (knowledge assessment segments)
• Stahl math proposal adjacency pairs in online
  chats
• Suthers meaning-making in group interactions
  meaningful interaction patterns in online
  discussions

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Ch. 2 Study Design
Class A 24 students - 6 tables Groups of 4 TI-84
Graphing Calculator Pen Pencil
Class B 24 students - 6 tables Groups of 4 TI-84
Graphing Calculator TI Navigator? system

• 2 Classes at CRDG laboratory school
• 10th grade mathematics classes
• Introduction of Navigator system into 1 class
• 4-week unit on Quadratics
• Same activities daily in each class

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Study Design Data Collection
Peer-grades
Unit Grade
Pretest
Posttest
Traditional Measures
7 Quizzes/Activities
Team Project
Research Measures
10 Self-Report Questionnaires
Daily Observations
Daily Conversation Tapings
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Ch. 3 Methods

• Primary data Group conversations
• Table 1 in class A for 18 days
• Table 1 in class B for 18 days
• Methodology
• Conversation Analysis of both groups
• CA comparison to Surveys and Achievement data
• Unit of Analysis/Observation Table Group

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Methods

• Unit of CA Episode of Learning (EL)
• EL can be positive or not
• Start a proposal or claim for group resolution
  or problem solving
• End verbalization of solution or resolution
• Looking for EL with knowledge gain

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Methods

• Possible code categories
• Learning Point specific piece of knowledge
  about quadratic expressions, calculator use, or
  general math understanding
• Argument agree, disagree
• Closure solution, answer, or resolution
  statement
• Linkage statement verbalized relationship
  between parts/functions
• Knowledge statement I get it, I dont
  understand, Oh, its that one
• Discussion Concept agreed topic of discussion
• KAP expression of knowledge, attitude,
  perception
• Analysis statement verbalizations of process
  for how something works

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Methods

• Possible factors for analysis
• Novice-Expert relations
• If problem solving verbalizations show influences
  on learning (esp., non-problem based influences
  such as attitudes, technology use)
• Analysis of EL that show knowledge gains
• Percent overall - similarities differences
  between those with and those without gain

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Methods

• Possible factors for analysis
• Matrix comparing discussion content to
  achievement

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Methods

• Possible factors for analysis
• Achievement Gains back-track for evidence of
  what factors assisted learning
• Context content around episode of learning
• Survey Analysis (coarse) do results suggest
  patterns in tech confidence, problem difficulties
• Performance Scores statistical analysis

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Conversation Analysis Analyzing Verbal Data

• Michelene Chi (1997), Quantifying qualitative
  analyses of verbal data A practical guide
• Methods for analyzing verbal data can overcome
  subjectivity and validity issues best method is
  mix of qualitative and quantitative to alleviate
  shortcomings of both.
• In analyzing verbal data for evidence of
  learning, the researcher is looking for evidence
  of a process over time To uncover what the
  student knows requires an analysis of the content
  of die verbal utterances (i.e., what the student
  said) along with a procedure to organize the
  content in some way (i.e., relate what is said)
  so that one can assess its overall structure.
  (p.273)

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Conversation Analysis Analyzing Verbal Data

• Chis guide for conducting a quantitative-based
  qualitative approach an 8-step method for
  coding and analyzing verbal data
• Reduce or sample the protocols (verbal stream)
• Segment protocols.
• Choose a Coding Scheme (or formalism)
• Operationalize evidence
• Depict the mapped formalism
• Seek patterns
• Interpret the patterns
• Repeat the process for new grain size and/or
  question

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CA Of Collaborative Learning

• Individual contributions to CA understanding
• Koschmann - Stahl - Suthers
• Nathan Dwyer
• Kathleen Berg
• Current research studies
• Aleven, et al. (2006) teaching metacognition
• Brna, (2006) debate on intelligent tutoring
  systems
• Cakir, et al. (2005) thread-based collaborative
  math problem solving in chat
• Choudhury, et al (2003) Learning communities and
  interacting agents
• Light (2006) adding method to explore meaning in
  human-computer interaction
• Luckin (2007) educational systems fit for use
  (context)
• Pon-Barry, et al. (2006) understanding student
  uncertainty in dialogue

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Early Observations

• The graphic calculator may facilitate computation
  and mathematics understanding, but method is
  unclear
• Students showed a lack of understanding of core
  math principles and an inability to transfer
  knowledge to real-world situations
• Significant gender differences in mathematics
  achievement, but cause or influence unclear
• Low retention of learning
• Teacher lack of inquiry-learning knowledge
  impaired effectiveness of new collaborative-learni
  ng technology -- curricula and technology need
  tight integration

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Study Critique

• Tighter procedures for measurements esp. surveys
  and coding observations
• Extend research on collaborative conversation to
  reveal how students are building each others
  knowledge under what conditions learning occurs
  through interactions
• Improve detail and extend coding of classroom
  observations to reveal impacts and results of
  environmental context, inter- and intra-group
  interactions and their impacts