Human-computer interaction factors critical to effective design of by rtu18834

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									 Human-computer interaction factors critical
to effective design of a model for computer-
mediated science-mathematics instruction



              Patricia J. Donohue
       Communication & Information Sciences,
              University of Hawaii



             pdonohue@hawaii.edu
           www2.hawaii.edu/~pdonohue
         HCI Factors

• When researchers find positive impacts
  on learning that can inform teachers, it
  is often lost to the classroom
  • Teachers do not have ready access to the
    information,
  • Do not understand the implications, or
  • Do not have guidelines how to use it.
            HCI Factors

• Purpose
  • Search the recent literature for components
    (artifacts, features, functionalities) from HCI
    studies that offer the most potential to aid
    instruction and learning while also providing
    practical possibilities for classroom application
• Perspective
  • The practicing classroom teacher’s point-of-view
  • Analyzing components for feasibility and
    practicality
    Table 1 - Components

Dim ension   Product/Components                 Pedagogical application                  Research Source
             SIETTE Adaptive Testing        Digitally distributed individualized    Conejo, Guzman, Milan, Trella,
                                            testing without teacher intervention    Perez-de-la-cruz, and Rios
                                                                                    (Conejo et al., 2004)
             3D virtual si mulations,       Student motivation                      Lester et al., 1999; Dowling,
             robotics, animated agents,     Effective content instruction           2001; Baylor; Gulz, 2004;
             collaboratories                Immersive learning environment          Vizcaino, 2005; Soller et al.,
                                            Aids retention and engagement                     a
                                                                                    2005; Sur weera & Mitrovic,
                                            Develop inquiry learning                                      ,
                                                                                    2004; VanLehn et al. 2005;
                                                                                    Robertson et al., 2004
             3D Animated Agents             Ethical Issues of:                      Zettlemoyer, Gregoire, and
                                            Constraints on agent autonomy           Bares, Lester (Lester, 1999)
Physical-                                   Constraints on motivation methods
Assistive    StoryStation                   Personalized individual guide through   Robertson, Cross, McLeod,
                                            content and thinking process            and Wiemer-Hastings
                                                                                    (Robertson et al., 2004)
             Simulated Student              Keeping students on-task                Baylor (Baylor, 2005)
                                            Guide/motivate student group work       Vizcaino (Vizcaino, 2005)
                                            Modeling le a rni n g be h avior
             Physi cal Aids & Assis tants   Speech recognition advances;            (Hudlicka, 2003; Picard et al.
                                            Multiple media modalities;              2004)
                                            Hardware/software assis tance
             Physi ological measures        Sensi ng and assessi ng physiological   Ikehara & Crosby (2002);
                                            states                                  Kapoor (2002)
    Table 1 - Components

Dimension   Product/Components                Pedagogical application                   Research Source

            Cognitive TutorŖ             Individualized skills model               Kenneth Koedinger (1998),
                                         Individualized math instruction &                                    s
                                                                                   Carnegie Mellon Universi tyÕ
                                         evaluation                                Carnegie Learning, Inc.
                                         Integration with curriculum
            Quantum Simulations, Inc.    Individualized problem-solving            See Johnson (2005) review in
                                         Just-in-time feedback                     Learning and Leading with
                                         Teaching mathematical thinking            Technology
                                         process
            Andes Physi cs Tutor         Step-by-step problem solving              VanLehn and colleagues
                                         No curriculum modification                (VanLehn et al., 2005)
            Arthur                       Collaborative design for individualized   Gilbert, Wilson, and Gupta
Cognitive                                instruction                               (Gilbert et al., 2005)
            Collaborative                Coaching function Š mapped to data        Soller, Martinez, Jerman, and
            management cycle             structures                                Muehlenbrock (Soller et al.
                                                                                   2005)
            Knowledge Sea II             Personalized intelligent resource         Brusilovsky, Farzan, and Ahn
                                         searching in a social context             (Brusilovsky et al., 2005)
            Modeling Theory              Cognitive model translatable to digital                     s
                                                                                   Ibrahim HallounÕ (Halloun,
            (Physi cs)                   model Š teaches scientific inquiry        2004) ModelingTheory in
                                                                                   Science Education
            Simulating instructor role   Adaptive, intelligent computer            (Baylor & Kim, 2005)
                                         si mulation of inst ruction following
                                         designed models
    Table 1 - Components

Dimension    Product/Components        Pedagogical application                   Research Source

            Measures of student   Engagement frameworks                    (Griffin & Symington, 1999;
            engagement            Characteristics of engagement            Ahn & Picard, 2005; Bangert-
                                  Models of detection and evaluation       Drowns & Pyke, 2002)
            Affective agency      Identifying attention, engagement             s
                                                                           MITÕ Affective Learning
                                  Affecting student motivation, interest   Laboratory (Picard et al., 2004)
Affective                         in learning and areas of most interest
                                  Establish and maintain trust
            Cognitive-Affective   Microbehavioral attributes integrating   (Nahl, 2006)
            Symbiotic Model       satisficingand optimizing affordances
                                  in a human-machine symbiosis

								
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