Cognitive diagnostic like approaches using neural-network analysis of serious educational videogames

Lamb, Richard; Annetta, Leonard A.; Vallett, David; Sadler, Troy D.

doi:10.1016/j.compedu.2013.08.008

Cited by 45 publications

(26 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, the artificial intelligence techniques have received favorable attention to develop general models in various scientific fields such as in business (Zhang and Wu, 2009), personalized and cognitive educational context (De Melo et al, 2014;Moridis and Economides, 2009;Lamb et al, 2012) and medical diagnosis (Jiang et al, 2010), solar radiation prediction (Shamshirband et al, 2016) , wind energy calculations (Medjber et al, 2016) and etc.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

Mohammadi

Shamshirband

Kamsin

et al. 2016

CATENA

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

Mohammadi

Shamshirband

Kamsin

et al. 2016

CATENA

View full text Add to dashboard Cite

“…Ideally, these data will make it possible to more accurately track student successes and also to customize feedback to learners at optimal time points for the support of learning (Goggins, Mascaro, & Valetto, ). We have examined trace data developed through student engagement within MBt with some interesting results related to latent cognitive attributes and science learning (Lamb, Annetta, Vallet, & Sadler, ). One of the takeaway messages from this work is that in order for these kinds of data to be most useful for both research and teaching purposes, learning analytics must be a part of overall system design.…”

Section: Discussionmentioning

confidence: 99%

Learning Biology Through Innovative Curricula: A Comparison of Game- and Nongame-Based Approaches

et al. 2015

Self Cite

View full text Add to dashboard Cite

This study explored student learning in the context of innovative biotechnology curricula and the effects of gaming as a central element of the learning experience. The quasi-experimentally designed study compared learning outcomes between two curricular approaches: One built around a computer-based game, and the other built around a narrative case. The research questions addressed student learning of basic biological principles, development of interest in learning science, and how a game-based approach compared to a nongame-based approach in terms of supporting learning. The study employed a pre-post design with 1,888 high school students nested within the classes of 36 biology teachers. Results indicated that students participating in both approaches demonstrated statistically and practically significant gains on both proximal and distal assessments of biological content knowledge. Neither group demonstrated gains in science interest. The curriculum by time interaction was not statistically different, indicating that students in both groups showed similar results. Implications for game-based science learning and future research include building better awareness of technological and professional development challenges associated with implementing educational games, the need for new strategies for understanding

show abstract

“…This method of collecting data about teaching and learning arises from the use of science based Serious Educational Games as conceptualized by Annetta (2010) to generate sufficient data for model development in this case n = 450,000 [29,33,34].…”

Section: Resultsmentioning

confidence: 99%

“…Identification of the attributes occurred using a modified cognitive diagnostics approach (CDA) [28]. The modification of the CDA is the use of video game server data based on student choices and actions as opposed to talk aloud protocols [29]. The number of neurons (seven) within the STAC-M clearly illustrates the level of comparative sophistication of the STAC-M in comparison to humans and even the simplest biological system.…”

Section: Student Task and Cognition Model (Stac-m)mentioning

confidence: 99%

“…One approach to isolation and modeling these complex systems is to choose one system and related cognitive pathway for modeling. A cognitive pathway is the sequential activation of particular brain systems processing specific data streams form internal and external antecedents [14]. The characteristics and complexity of cognitive pathways lend themselves to modeling via artificial neural networks (ANN) [15].This level of complexity is incredibly difficult to model even when using systems which are best suited for models of this complexity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

Lamb

Premo

2015

Computation

View full text Add to dashboard Cite

Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA) can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M) and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a "virtual" student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M) a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning.

show abstract

Cognitive diagnostic like approaches using neural-network analysis of serious educational videogames

Cited by 45 publications

References 37 publications

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

Learning Biology Through Innovative Curricula: A Comparison of Game- and Nongame-Based Approaches

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

Contact Info

Product

Resources

About