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Laboratory for Interactive Learning Technologies Department of Information and Computer Sciences University of Hawai’i Communicative Dimensions of End-User Environments Christopher Hundhausen LILT Lab University of Hawai’i Honolulu, HI USA [email protected] Sarah Douglas HCI Lab University of Oregon Eugene, OR USA [email protected] Introduction Worthy goal: Design effective end-user environments (EUE’s) But what does “effective” mean? Traditional view: Effective = enhanced human performance (Green & Petre, 1996) Fewer “programming games” and “hard mental operations” Faster learning Better program comprehension (“role expressiveness”) Low viscosity (easy to make changes) Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Introduction (cont.) Alternative View: Effective = Enhanced Human Communication (Collaborative EUE’s) Education: facilitate construction of shared knowledge (Roschelle, 1990; Suthers, 1999) Software/Web Design: help build design consensus (Landay & Myers, 1995; Lin et al., 2000; Damm et al., 2000) Our work: Algorithm Visualization EUE’s Students construct and present their own visualizations Research Questions How might EUE’s impact human communication? What specific design features make them well suited to facilitating human communication? How might communication-supporting EUE’s differ from conventional EUE’s? Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Talk Outline 1. Empirical Foundation: Ethnographic Studies 2. Provisional Framework of “Communicative Dimensions” 3. Design Implications 4. Conclusions 5. Future Work Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Ethnographic Studies Studied Two Semesters of Undergraduate Algorithms Course with Visualization Construction and Presentation Assignments Study I Used SAMBA (Stasko, 1997) to construct input- general visualizations Required 33.2 hours on average (n = 20) Discussed programming toil in presentations Study II Used simple art supplies (pen, paper, scissors, etc.) to construct “one-shot” visualizations Required 6.2 hours on average (n = 20) Discussed underlying algorithm in presentations Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Communicative Dimensions 1. Programming Salience Extent to which programming act focuses on relevant domain concepts Low-level (graphics) programming generates discussion about programming details Domain-specific programming generates discussion about domain concepts 2. Typeset Fidelity Extent to which program resembles textbook figure Sketched appearance (low fidelity) invites discussion, whereas polished (high fidelity) appearance discourages it See also Schumann et al.’s (1996) architecture study Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Communicative Dimensions (cont.) 3. Story Content Extent to which program portrays domain in terms of story Programs with story content stimulate livelier discussion that purely geometric/textual programs 4. Modifiability Extent to which program can be dynamically altered Highly modifiable programs are better able to mediate discussions about domain concepts than static programs; they facilitate audience participation Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Communicative Dimensions (cont.) 5. Controllability Flexibility with which a presenter can control a program’s execution Forwards and backwards execution Jump to arbitrary point High controllability enables presenter to dynamically respond to audience’s questions and comments 6. Referencability Ease with which conversational participants can refer to elements of program Highly referencabable program facilitates communication by serving as resource for disambiguating contextual references (e.g., “that”) Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Design Implications Dimension Inhibit Communication Support Communication Programming Salience Low-level prog. Domain-spec. prog. Typeset Fidelity Polished graphics Sketched graphics Story Content Prohibit storyline Support storyline Modifiability recompilation Dynamic modification Controllability Forwards only Forwards + backwards Referencability Mouse pointer only Dynamic mark-up Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Example EUE’s on Each End of Continuum Conventional: SAMBA (Stasko, 1997) 1. Annotate extern MyAnimator football_bsort; main(int argc, char *argv[]) { int n,i,j,temp,a[50],count; char str[100]; . . . football_bsort.Ready(n); for (j=n-2; j>=0; --j) { football_bsort.StartOuter(); for (i=0; i<=j; ++i) { football_bsort.Compare(i,i+1); . . . } football_bsort.InPlace(j+1); } . . . } football_bsort::Compare(int i, int j) { //write SAMBA commands to //script file outfile << "{\n“ << "moveto" << player[i] << " " << step_up_loc[i] << "\n" << "moveto " << ball << " " << step_up_ball_loc[i] << "\n" << "}\n" << "delay 7\n"; } 2. Compile 3. Execute Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Example EUE’s on Each End of Continuum (cont.) Communication-Supporting: ALVIS (Hundhausen, 1999) Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Summary End-User Environments Can Play Important Role in Facilitating Human Communication Communicative Dimensions Provide Important Extension to Green & Petre’s Cognitive Dimensions Limitations Plainly preliminary! Apply mainly to collaborative EUE’s (e.g. learning and design environments) Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Future Directions Empirically Establish Cause-Effect Relationships Between Design Features and Communicative Activity Ground Firmly in Underlying Theory of Communication Expand by Consolidating with Other Lines of Empirical Work [e.g., Suthers’s (1999) “Representational Guidance” Hypothesis] Christopher Hundhausen ● Laboratory for Interactive Learning Technologies ● University of Hawai’i ● [email protected] Thank you Christopher Hundhausen [email protected] http://lilt.ics.hawaii.edu/~hundhaus Information and Computer Sciences University of Hawai’i Human-Computer Interaction Software Engineering Networking http://www.ics.hawaii.edu Educational Technologies Artificial Intelligence Multimedia