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Design and Evaluation for the Future of m-Interaction

2009· book-chapter· en· W591772795 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldComputer Science
TopicUsability and User Interface Design
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsUsabilitySoftware portabilityMobile technologyContext (archaeology)Interaction designMobile deviceComputer scienceHuman–computer interactionResource (disambiguation)Internet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

Mobile technology has been one of the major growth areas in computing over recent years (Urbaczewski, Valacich, & Jessup, 2003). Mobile devices are becoming increasingly diverse and are continuing to shrink in size and weight. Although this increases the portability of such devices, their usability tends to suffer. Fuelled almost entirely by lack of usability, users report high levels of frustration regarding interaction with mobile technologies (Venkatesh, Ramesh, & Massey, 2003). This will only worsen if interaction design for mobile technologies does not continue to receive increasing research attention. For the commercial benefit of mobility and mobile commerce (m-commerce) to be fully realized, users’ interaction experiences with mobile technology cannot be negative. To ensure this, it is imperative that we design the right types of mobile interaction (m-interaction); an important prerequisite for this is ensuring that users’ experience meets both their sensory and functional needs (Venkatesh, Ramesh, & Massey, 2003). Given the resource disparity between mobile and desktop technologies, successful electronic commerce (e-commerce) interface design and evaluation does not necessarily equate to successful m-commerce design and evaluation. It is, therefore, imperative that the specific needs of m-commerce are addressed–both in terms of design and evaluation. This chapter begins by exploring the complexities of designing interaction for mobile technology, highlighting the effect of context on the use of such technology. It then goes on to discuss how interaction design for mobile devices might evolve, introducing alternative interaction modalities that are likely to affect that future evolution. It is impossible, within a single chapter, to consider each and every potential mechanism for interacting with mobile technologies; to provide a forward-looking flavor of what might be possible, this chapter focuses on some more novel methods of interaction and does not, therefore, look at the typical keyboard and visual display-based interaction which, in essence, stem from the desktop interaction design paradigm. Finally, this chapter touches on issues associated with effective evaluation of m-interaction and mobile application designs. By highlighting some of the issues and possibilities for novel m-interaction design and evaluation, we hope that future designers will be encouraged to “think out of the box” in terms of their designs and evaluation strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.301
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it