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Fusing physical and cognitive spaces: Using wireless networked sensors to assess the who, what, where, when, and how of student learning Gregory K. W. K. Chung UCLA/CRESST Mani B. Srivastava Department of Electrical Engineering, UCLA

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Fusing physical and cognitive spaces: Using wireless networked sensors to assess the who, what, where, when, and how of student learning

Gregory K. W. K. ChungUCLA/CRESST

Mani B. SrivastavaDepartment of Electrical Engineering, UCLA

Annual Conference of the National Center for Research on Evaluation, Standards, and Student Testing (CRESST)

September 14-15, 2000

Los Angeles, CA


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Measuring Behavior networked sensors to assess the who, what, where, when, and how of student learning

  • Current techniques

    • Real-time observation with sampling

    • Observation of video or audio taped data

    • Characteristics

      • Are time-consuming and prone to error

      • Rarely capture temporal properties of behavior

      • Major advantage: human in-the-loop categorizing of observations


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Measuring Behavior networked sensors to assess the who, what, where, when, and how of student learning

  • Sensor-based techniques

    • Computationally measure physical properties of person and related objects

    • Computationally derive observations from sensor data

    • Vast improvement in observation capabilities

      • Scalability (high number of observations)

      • Efficiency (more information / unit cost)

      • Timeliness (rapid turnaround time)

      • Accuracy


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Measuring Behavior networked sensors to assess the who, what, where, when, and how of student learning

  • Sensor-based techniques (continued)

    • Measuring the who, what, where, when, and how of human-human and human-object interactions

    • Key challenges:

      • Develop algorithms to support the aggregation of sensor data that accurately measure the construct of interest, are meaningful, are credible, and are in a form usable to different end-users

      • Relate behavioral measurements to cognitive processes and task outcomes

      • Approximate the 24/7 human observer


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Wireless Networked Sensors networked sensors to assess the who, what, where, when, and how of student learning

  • Wireless networked sensors

    • Integrate sensing and short-range communication function in a single unit

      • Low-power consumption (long operational life)

      • Small form factor (embed in everyday objects)

      • RF (avoid line of sight problems)

    • Tetherless bi-directional connection to the Internet

      • Remote measurement and control capability

      • Embed “intelligence” and interactivity in everyday objects


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Sample of Sensor Types networked sensors to assess the who, what, where, when, and how of student learning

  • Acoustic

  • Light

  • Image/video

  • Touch/pressure

  • Temperature

  • Identification

  • Position (x,y,z)

  • Proximity (x’,y’,z’)

  • Orientation (360°)

  • Movement (acceleration)


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Potential Application networked sensors to assess the who, what, where, when, and how of student learning

  • Describing interaction

    • Student-object

    • Student-student

    • Student-teacher

    • Teacher-object

  • Triangulate multiple measures of interaction to successively refine inferences about interaction


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Example: Deriving observations of small group object categorization task

Object and student position, student orientation, object- proximity data allow the following questions to be answered:

S1

1. How many objects are categorized correctly by shape? (12-squares, triangles, circles)

2. What object are students focused on? (rhombus)

3. How many objects remain to be categorized? (1-rhombus)

S2

S3


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Example: Deriving observations of small group instruction categorization task

Position, orientation, acoustic data allow the following questions to be answered:

S1

T

1. Who is paying attentionto the teacher? (S1, S2)

2. Which students are participating? (S1, S2)

3. What is the nature of the utterance? (S2 - question)

4. Which students are not paying attention orparticipating? (S3, S4)

???

S2

S4

S3


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Potential Application categorization task

  • Describing the classroom environment

    • Measures of:

      • Amount of lecture, independent, small-group instruction

      • Student resource use

      • Student roaming profiles

      • Teacher-student interaction

      • Student-student interaction

      • Student attention


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Next Steps categorization task

  • NSF Information Technology Research Grant (2000-2002)

    • UCLA Electrical Engineering lead department (PI Srivastava), UCLA Computer Science department and CRESST are partners

    • Develop technology wireless protocols, network architectures, middleware architecture, data management and mining, user profiling, speech recognition

    • Application domain: Assessing young children’s (K-1) problem-solving development


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Next Steps categorization task

  • Qualitative analyses of classroom, children’s interactions with each other, and children’s interaction with objects

    • Develop measures using sensor data

    • Validate measures with human observations

  • Develop sensor-based assessment of children’s problem-solving skills

    • Use play or other manipulative-based task that requires demonstration of performance

    • Use extended task to gather data over time


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