A comparison of electronic to traditional pen-and-paper data collection in discrete trial training for children with autism

Tarbox, Jonathan; Wilke, Arthur E.; Findel-Pyles, Rachel S.; Bergstrom, Ryan; Granpeesheh, Doreen

doi:10.1016/j.rasd.2009.07.008

Cited by 29 publications

(27 citation statements)

References 6 publications

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“…However, to date, there have been few research reports on the quality of data produced by human observers using continuous recording, either electronic or pen-and-paper. Tarbox, Wilke, Findel-Pyles, Bergstrom, and Granpeesheh (2010) compared penand-paper recording with touchscreen personal digital assistant (PDA) recording during discrete trial training. Using interobserver agreement (IOA), they compared the observers' records with the experimenters' records and concluded that there was little difference in accuracy between observers recording trial-by-trial data using either PDA or pen-and-paper recording during discrete trial training.…”

mentioning

confidence: 99%

Using Calibration and Interobserver Agreement Algorithms to Assess the Accuracy and Precision of Data From Electronic and Pen‐and‐paper Continuous Recording Methods

Phillips

Mudford

Zeleny

et al. 2014

Behavioral Interventions

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Often it is assumed that electronic recording by observers necessarily provides better quality data than pen-and-paper methods. Fifteen novice observers recorded rates of responding from 10 role-played video samples using one of three continuous recording input formats: keyboard (laptop), touchscreen (personal digital assistants), or pen-and-paper. We evaluated the quality of the observers' data compared with criterion records using calibration and interobserver agreement algorithms. Results of the calibration analysis revealed that observers in the touchscreen group produced the most consistently accurate and precise data, the keyboard group observers showed wide variation in precision and accuracy, and the pen-and-paper group observers were significantly less precise than the touchscreen group. We conclude that although electronic recording has the potential to be as accurate as, and more precise than, pen-and-paper methods, this is far from guaranteed. Analyses of observers' errors advise recommendations for improving data accuracy and precision when using each method.

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mentioning

confidence: 99%

Using Calibration and Interobserver Agreement Algorithms to Assess the Accuracy and Precision of Data From Electronic and Pen‐and‐paper Continuous Recording Methods

Phillips

Mudford

Zeleny

et al. 2014

Behavioral Interventions

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“…Their conclusions were that data accuracy are equal for both formats, but traditional data collection was faster [21]. However, we claim that rapidity mainly relies on the software features and the usability of user interfaces (UIs) as well as on the subjective user's ability to insert data.…”

Section: Related Workmentioning

confidence: 90%

An Analytic Tool for Assessing Learning in Children with Autism

Bartalesi

Buzzi

et al. 2014

Lecture Notes in Computer Science

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Abstract.One approach for teaching subjects with autism is Applied Behavior Analysis (ABA). ABA intervention aims to model human behavior by observing, analyzing and modifying antecedents and/or consequences of a target behavior in the environment. To achieve this, many data are recorded during each trial, such as subject response (correct/incorrect, level of prompt, inappropriate behavior, etc.). In this paper we present a web application that aggregates and visualizes data collected during technology-enhanced educational sessions, in order to monitor learning in children with autism. In a previous study we developed a free open source web application called ABCD SW, to support educators in administering ABA programs. In this study we present a learning analytic tool that retrieves, aggregates and shows --in graphical and table form --data gathered by ABCD SW. This software offers accurate real-time monitoring of children's learning, allowing teachers to analyze the collected data more rapidly, and to accurately tune and personalize the intervention for each child.

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“…Current solutions include: mTrial, a program developed to allow therapists to record, store, and report discrete trial data on a personal data assistant [7]. The study found that traditional data collection using pen and paper was approximately equal in accuracy to mTrial but surprising was faster than the digital alternative.…”

Section: Current Solutionsmentioning

confidence: 99%

Towards designing assistive software applications for discrete trial training

Picardo

Metson

Hoda

et al. 2014

Companion Proceedings of the 36th International Conference on Software Engineering

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Discrete Trial Training (DTT) is one of the most effective training methods for children diagnosed with Autism. Traditional DTT suffers from limitations of inconsistencies on account of human error, disruptions due to in-session data collection by trainers, and difficulties of producing physical within-stimulus prompts. Current software solutions either support sole child usage thereby eliminating the social interaction benefits of DTT or lack automated data collection. Designed by an inter-disciplinary team of software engineers, HCI, and psychology experts and certified behaviour analysts for a touch-tabletop, DTTAce is an assistivesoftware that provides digital consistency and integrity and supports customization of trials, automated data collection, and within-stimulus prompts while preserving natural interactions and the social nature of DTT. It is an important step towards designing effective assistive software for Discrete Trial Training.

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A comparison of electronic to traditional pen-and-paper data collection in discrete trial training for children with autism

Cited by 29 publications

References 6 publications

Using Calibration and Interobserver Agreement Algorithms to Assess the Accuracy and Precision of Data From Electronic and Pen‐and‐paper Continuous Recording Methods

Using Calibration and Interobserver Agreement Algorithms to Assess the Accuracy and Precision of Data From Electronic and Pen‐and‐paper Continuous Recording Methods

An Analytic Tool for Assessing Learning in Children with Autism

Towards designing assistive software applications for discrete trial training

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