Identifying and Evaluating Language-Learning Technology Tools

Identifying and Evaluating Language-Learning Technology Tools

Farideh Nekoobahr (The University of Houston, USA), Jacqueline Hawkins (The University of Houston, USA), Kristi L. Santi (The University of Houston, USA), Janeen R. S. Antonelli (The University of Houston, USA) and Johanna Leigh Thorpe (The University of Houston, USA)
DOI: 10.4018/978-1-7998-3476-2.ch010
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Digitization and the globalization of English have made it possible to incorporate different forms of digital technology into the infrastructure of English language programs. However, there are no clear criteria in the existing literature to identify and evaluate appropriate language-learning technology tools. To fill the gap, this project proposed empirically supported guidelines in a rubric called the ULTIA Rubric to facilitate and accelerate the process of identifying and evaluating technology-supported language-learning tools. The ULTIA Rubric has its basis in the major components of the five concepts of universal design for learning (UDL), learning science (LS), technology acceptance model (TAM), intelligent tutoring system (ITS), and automatic speech recognition (ASR). The rubric can function as a practical solution for program administrators, instructors, and English language learners (ELLs) who are seeking a reliable roadmap to evaluate language-learning software.
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Universal Design for Learning (UDL)

In education, UDL refers to “a set of principles for curriculum development” that provides “effective instruction to all learners” and gives them “equal opportunities to learn” (National Center on UDL, 2014). The purpose of UDL instruction is to create a motivating “learning environment that challenges and engages all students” (The IRIS Center, 2017).

Key Terms in this Chapter

ELL: English language learners (ELLs) are students who attend English language programs in order to acquire English language proficiency. Either school-based or postsecondary ELLs have to meet all the standards and requirements that other students in schools and colleges are expected to meet.

MALL: Mobile-assisted language learning refers to applications of smartphones in teaching languages.

ASR: Automatic speech recognition (ASR) is a technology that identifies and decodes spoken words and transcribes them into text.

UDL: Unlike traditional, one-size-fits-all instruction, universal design for learning (UDL) is a systematic and flexible framework which creates a motivating learning environment to challenge and engage all learners.

ULTIA: The ULTIA Rubric which is exclusively applied in this project and is registered with the US Copyright Office (registration number: TXu 2-095-958) refers to the five concepts of Universal Design for Learning (UDL), Learning Science (LS), Technology Acceptance Model (TAM), Intelligent Tutoring System (ITS), and Automatic Speech Recognition (ASR). The ULTIA Rubric has been used to systematically and scientifically identify and evaluate language-learning technology tools. Each letter of the acronym ULTIA refers to the first of the first word letter of the five concepts of UDL, LS, TAM, ITS, and ASR respectively.

CALL: Computer-assisted language learning refers to applications of the computer in teaching languages.

LS: Learning science (LS) theories are valid and empirically supported theories that are the instructional foundations applied for all kinds of learners.

TAM: Technology acceptance model (TAM) is applied in information systems to predict the extent to which users think they can perform better in a system by using technology.

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