Measuring Human Intelligence with Artificial Intelligence: Adaptive Item Generation

Embretson, Susan E.

doi:10.1017/cbo9780511607073.014

Cited by 16 publications

(23 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ideally, the learning measures we implement in future studies should be developed entirely from scratch-and then iteratively refined-using a cognitive processing model, such as Embretson's (1998Embretson's ( , 2002Embretson's ( , 2005 Cognitive Design System, or the EvidenceCentered Design model (Behrens et al 2004;Mislevy et al 2003). Using a variety of protocols, such as task analysis, we need to closely examine the cognitive processes taking place as participants interact with the SimLandia environment and curriculum, in order to establish a refined list of constructs for learning and motivation from a cognitive information processing perspective.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Collaboration modality, cognitive load, and science inquiry learning in virtual inquiry environments

Erlandson

Nelson

Savenye

2010

Education Tech Research Dev

View full text Add to dashboard Cite

Educational multi-user virtual environments (MUVEs) have been shown to be effective platforms for situated science inquiry curricula. While researchers find MUVEs to be supportive of collaborative scientific inquiry processes, the complex mix of multi-modal messages present in MUVEs can lead to cognitive overload, with learners unable to effectively process the rich information encountered in virtual space. In this study, we investigated the effect of communication modality on cognitive load and science inquiry learning in students completing a science inquiry curriculum in an educational MUVE. Seventy-eight undergraduate education majors from a large southwestern university participated in this control-treatment study. Significant positive results were found for reducing cognitive load for participants communicating through voice-based chat, although this reduction was not found to influence learning outcomes. We conclude that use of voice-based communication can successfully reduce cognitive load in MUVE-based inquiry curricula.Keywords Virtual environments Á Cognitive load Á Collaboration Á Modality Á VoIP One alternative to classroom-based inquiry that has arisen in recent years is curricula embedded within computer-based educational multi-user virtual environments (MUVEs). MUVEs have emerged as a fertile platform for situated inquiry learning environments, especially concerning science inquiry learning processes (Barab et al. 2005;Nelson et al. 2005;Nelson 2007). Briefly, educational MUVEs are three-dimensional (3-D) virtual worlds that afford learners interaction with other humans and software-based agents, both represented through characters called avatars, as part of an open-ended exploratory environment. In these environments, learners can interact with various situated objects (including images, sounds, and other multimedia content) while communicating and collaborating with other learners to investigate scenarios and solve problems of varying complexity (Nelson and Ketelhut 2007).de Jong (2006) notes that one of the biggest challenges for supporting inquiry learning with technology is the seamless integration of collaborative learning and inquiry learning. Another challenge, cited by Kirschner et al. (2006), is that guided inquiry in technologybased environments is more powerful than wholly open-ended exploration. Both of these challenges for science inquiry learning-appropriately guided inquiry learning and integration of collaboration and inquiry learning-are being addressed by researchers implementing MUVEs (e.g., Bers 1999;Corbit 2002;Slator et al. 2004;Dede et al. 2002). In an even broader sense, research in this area is part of a rapidly expanding focus on the use of educational gaming environments for learning (Gee 2003). MUVEs as situated inquiry environmentsSadler et al. (2007) have outlined the need for emphasis upon socioscientific inquiry, or an approach to scientific inquiry learning that includes social and emotional constructs in addition to the typical cognitive skills involved with the...

show abstract

Section: Limitations and Future Researchmentioning

confidence: 99%

Collaboration modality, cognitive load, and science inquiry learning in virtual inquiry environments

Erlandson

Nelson

Savenye

2010

Education Tech Research Dev

View full text Add to dashboard Cite

show abstract

“…Within this framework the construction of an item generator starts with a definition of the latent trait and the cognitive processes that characterize it. This process is referred to as the construction of a cognitive model (Embretson, 1998(Embretson, , 2005Gorin, 2006). The psychometrician then chooses an item format to measure the latent trait.…”

Section: Introductionmentioning

confidence: 99%

“…Afterwards, the cognitive model is condensed into a more specific cognitive item model by outlining the item features that are assumed to trigger the cognitive processes used to solve the test items (e.g. Arendasy & Sommer, in press;Arendasy et al, 2008;Embretson, 1998Embretson, , 2005Gorin, 2006). These item features are commonly referred to as radicals (Irvine, 2002).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the contribution of different item features to the effect size of the gender difference in three-dimensional mental rotation using automatic item generation

Arendasy

Sommer

2010

Intelligence

View full text Add to dashboard Cite

“…Although both methods involve an experimental manipulation of item design features and the mathematical modeling of their effect on either response latencies (cognitive component analysis), or item difficulties, automatic item generation has shown to bypass several limitations of cognitive component analysis by means of more elaborated psychometric models used to analyze the data (cf. Arendasy & Sommer, in press-a;Embretson, 1998Embretson, , 2005Irvine & Kyllonen, 2002).…”

Section: Introductionmentioning

confidence: 99%

Combining automatic item generation and experimental designs to investigate the contribution of cognitive components to the gender difference in mental rotation

2010

View full text Add to dashboard Cite

Measuring Human Intelligence with Artificial Intelligence: Adaptive Item Generation

Cited by 16 publications

References 24 publications

Collaboration modality, cognitive load, and science inquiry learning in virtual inquiry environments

Collaboration modality, cognitive load, and science inquiry learning in virtual inquiry environments

Evaluating the contribution of different item features to the effect size of the gender difference in three-dimensional mental rotation using automatic item generation

Combining automatic item generation and experimental designs to investigate the contribution of cognitive components to the gender difference in mental rotation

Contact Info

Product

Resources

About