Persistent trends of declining or stagnant reading proficiency among fourth- and eighth-grade students in the U.S. highlight the need for effective evidence-based reading instruction that meets the needs of students and of teachers (U.S. D.O.E., 2019). Over the past few decades, educational technology for reading and language learning has become an integral component of literacy instruction. Today, the use of software programs, mobile applications, interactive websites, and video-based platforms for language and literacy learning in K–12 classrooms is a promising means of increasing student achievement in reading.
Modern advances in computer science, machine learning, and artificial intelligence (AI) coupled with literacy instruction have led to the development of Amira Learning®, an automated AI–powered reading tutor that delivers targeted instruction, practice, and assessment in early learners’ literacy skills.
This document highlights the foundational research supporting Amira Learning. It provides an overview of the research underlying Amira Learning’s AI–powered intelligent reading tutor and the research on key elements of early literacy instruction. It describes the components of the Amira Learning pedagogy and the research base supporting each component. The paper also outlines the role of professional development in empowering teachers to effectively integrate Amira Learning into the class flow.
Powered by artificial intelligence (AI) and evidence-based best practices, Amira Learning is a reliable classroom assistant that assesses oral reading fluency (ORF), screens for dyslexia, and provides reading practice.
Amira Learning stems from decades of research and development conducted by scientists at Carnegie Mellon University’s Project LISTEN. In 1997, Project LISTEN researchers first introduced the Reading Tutor, a computer-based instructional program that used artificial intelligence technology to listen to children read aloud, analyze the accuracy and fluency of each student’s reading, and deliver targeted instruction and feedback to each student (Aist & Mostow, 1997; Mostow, 2012). Since the initial introduction of the Reading Tutor, Project LISTEN scientists have collaborated with leading researchers in reading science, speech recognition, and psychometrics to develop Amira Learning.
Technology has permeated the classrooms and schools within the past decade at a rapid rate, transforming the way students learn, educators teach, and administrators manage resources and interpret data. Increased numbers of tablets and laptops in the hands of students, enhancements made on mobile devices, inclusion of multimedia on websites, and the infusion of social media in students’ daily lives have altered the very nature of reading. Traditional print books are steadily being replaced by eBooks, audiobooks, online news sources, and even voice-controlled intelligent personal assistant services that provide an immediate answer to a spoken question. In these ways, students access text through more modalities than in the past.
Advances in the fields of artificial intelligence, human-computer interaction and hardware systems, and the development of “intelligent” computer-based assessments and instruction, now known as the Intelligent Tutoring System, have evolved from computer laboratories and are steadily being implemented into mainstream classrooms with positive results.
ARTIFICIAL INTELLIGENCE AND LITERACY INSTRUCTION
Artificial intelligence scientists have been developing intelligent machines that can perform functions like speech recognition, adaptive learning, and advanced problem solving. Artificial intelligence is increasingly being integrated with common technology used within our daily lives, particularly embedded speech recognition software—smartphones, smart watches, smart speakers, and smart cars, to name a few. Although artificial intelligence has been researched since the 1940s in academic laboratories, its application into mainstream schools and Tier 1 classrooms within the past two decades is becoming more widespread, showing promising results. In the area of literacy, AI tools hold great potential, especially for developing students’ reading and writing proficiency.
Recent market research predicts that the use of AI in the field of education will grow 47.5% through 2021 (Research and Markets, 2018). One of the driving forces of the widespread uses of AI in education is providing students with adaptive learning paths and integrating AI in educational games to enhance interactivity and motivation. There are numerous ways AI has the potential to transform the educational landscape (eSchool News, 2017; Utermohlen, 2018):
When AI software is implemented effectively within a classroom and students are engaged with online practice on the computer, the classroom teacher is freed to concentrate efforts on individual student needs or to provide targeted small-group instruction. Because AI-based software provides teachers with electronically collected and organized information about students’ individual work, the data can be extremely useful for individualizing instruction.
AUTOMATED SPEECH RECOGNITION AND LITERACY INSTRUCTION
A significant technological advance that has enabled the development of intelligent reading tutors is automated speech recognition software, which listens to users’ oral reading and then provides context-specific feedback (Mostow & Aist, 2001). Automated speech recognition software has shown to be a promising digital technology to enhance students’ reading proficiency particularly in the following areas (Mostow & Aist, 1999):
Amira Learning’s automated speech recognition capabilities stem from decades of Project LISTEN research in continuous speech recognition (Huang et al., 1993), speech analysis techniques (Mostow et al., 1994), and interactive educational multimedia design (Mostow et al., 1995). Using speech samples from fluent adult speakers and from children, Project LISTEN researchers have generated models of fluent oral reading and identified specific syntactic and lexical features of text that can be used to predict fluency and comprehension and to identify targets for instructional intervention and remediation (Mostow, 2012; Sitaram & Mostow, 2012).
INTELLIGENT TUTORING SYSTEMS AND LITERACY INSTRUCTION
Advances in computer science and artificial intelligence gave rise to “intelligent” computer-based instruction programs beginning in the 1970s (Corbett, Koedinger, & Anderson, 1997). Traditionally, human tutors are experts that hold deep knowledge and understanding of a subject matter domain and also of studens’ learning goals (Reed & Meiselwitz, 2011). Modeled on effective human tutors, intelligent tutoring systems are computer software programs that use AI to provide a personalized, adaptive, and interactive learning experience within a one-on-one tutor-student relationship; like human tutors, intelligent tutoring systems seek to engage students in sustained learning activities and to interact with each student based on a deep understanding of individual needs and preferences (Anderson, 1982; Corbett, Koedinger, & Anderson, 1997).
Advantages of Intelligent Tutoring Systems
Researchers from the fields of cognitive psychology and computer science have long been interested in the differences between human tutors and intelligent tutoring systems.
Studies have demonstrated significant improvements in students’ literacy achievement for one-on-one literacy tutoring (Snow, Burns, & Griffin, 1998). Some characteristics of individualized tutoring are as follows:
Individual human tutoring demonstrates positive effects with specific reading and writing tasks, and many times, the benefits are long lasting.
However, studies of the behavior of human tutors show that they are less likely to ask questions designed to diagnose students’ misconceptions (McArthur, Stasz, & Zmuidzinas, 1990), to know which false beliefs their students held (Chi, Siler, & Jeong, 2004), and to change their behavior and practices when given detailed diagnostic information about their students’ misconceptions and false beliefs (Sleeman et al., 1989). Studies found high variability in human tutors’ behaviors towards their students, as compared to intelligent tutors that had been programmed for consistency (Reeder et al., 2015). Therefore, human tutoring is time-consuming, variable in its quality of instruction, and likely extremely expensive.
Fortunately, advances in technology that assist in enhancing students’ literacy skills provide a robust and cost-effective method to help achieve reading success—namely, automated individual literacy tutoring (Mostow et al., 2002). In a study measuring the effectiveness of an intelligent reading tutor 20 minutes a day compared to 30 minutes or more a day with a human tutor over a six-week period, results demonstrated that the group with the intelligent reading tutor offered time efficiencies over convential human tutoring (Reeder et al., 2015).
Children with reading difficulties often fail to realize when they misidentify a word. This problem is especially prominent in striving readers and children with weak metacognitive skills. Therefore, intelligent reading tutors have the ability to detect students’ errors while reading connected text and can, therefore, provide the support the students’ need as they’re reading.
Therefore, study findings highlight ways in which AI-powered intelligent tutoring systems can serve to improve efficiency and reduce inconsistencies in the delivery of remediation and intervention in core academic subjects (Reed & Conklin, 2005).
Use of the Avatar in Intelligent Tutoring
Amira Learning uses an AI-powered avatar named Amira to communicate and interact with students on the platform. An avatar is an animated pedagogical agent that interacts with students and helps them learn by providing hints, clues, feedback, and instruction (McNamara et al., 2009). Research has shown that the use of an avatar in online and virtual learning environments provides a degree of social presence and creates a sense of community for learners (Annetta & Holmes, 2006), and that social presence is a strong indicator of participants’ satisfaction with computer-mediated communications (Gunawardena & Zittle, 1997; Allmendinger, 2010). By using realistic avatars that communicate with students via expressions, gestures, and visuals, intelligent tutoring systems can enhance human-computer interactions and thus increase student-tutor engagement (Basori et al., 2011).
EVIDENCE FOR AMIRA LEARNING
The effectiveness of Amira Learning has been demonstrated in gold-standard randomized controlled field experiments in real-world classroom settings. Experimental studies have found that students randomly assigned to use the Project LISTEN Reading Tutor made greater reading gains than students in the control conditions who: (a) used a comparable commercial reading software program (Mostow et al., 2003), (b) were taught by a human reading tutor (Aist et al., 2001; Mostow et al., 2001), (c) participated in sustained silent reading (Mostow et al., 2002), or (d) received “business as usual” classroom instruction (Mostow et al., 2003).
Studies have also shown that Amira Learning is effective for English learners. A study with elementary school students from Spanish-speaking homes in Chicago found that the Project LISTEN Reading Tutor led to significantly greater gains in reading fluency than did the control condition of sustained silent reading (Poulsen et al., 2007). Researchers at the University of British Columbia found that elementary and middle school students from Hindi/Urdu-, Mandarin-, and Spanish-speaking homes who received the Reading Tutor made significant gains on the Word Attack, Word Identification, Word Comprehension, and Passage Comprehension subtests of the Woodcock Reading Mastery Tests-Revised (Reeder et al., 2007; Reeder et al., 2008). Results from a recent follow-up study with elementary and middle school students in Vancouver, Canada indicated that students who used the Reading Tutor made significant gains in oral reading fluency, and that the gains were slightly larger than those made by students in the control condition who received regular classroom instruction with English learning support (Reeder et al., 2015).
SIMPLE VIEW OF READING
The Simple View of Reading is a prominent theory of reading development that was proposed by educational psychologists Philip Gough and William Tunmer in 1986. According to the Simple View of Reading, reading comprehension is the product of word recognition and language comprehension. In order to read with comprehension, readers must simultaneously decode the words on a page while drawing on their knowledge of language to access the meaning of the text. Decoding involves connecting the spellings in words to their sounds and putting them together in order to read.
In 2001, reading scientist Hollis Scarborough elaborated on the simple view framework to develop the Strand Model of Skilled Reading—also referred to as the Reading Rope. According to the Strand Model, each component of the Simple View of Reading—word recognition and language comprehension—is itself a multifaceted skill. The word recognition strand encompasses phonological awareness, decoding, and sight recognition, while the language comprehension strand includes background knowledge, vocabulary, language structures, verbal reasoning, and literacy knowledge. Given instruction and practice, the word recognition skills become more automatic while the language comprehension skills become increasingly strategic.
FIVE PILLARS OF LITERACY
In 1997, the United States Congress convened the National Reading Panel to review the scientific research evidence on reading and the resulting implications for reading instruction. In 2000, the experts on the panel produced a report based on decades of research evidence that highlighted five key pillars of early literacy and reading instruction: Phonemic Awareness, Phonics, Fluency, Vocabulary, and Comprehension (National Institute of Child Health and Human Development (NICHD), 2000). Numerous independent studies and expert panels have concluded that phonemic awareness and phonics have a direct and positive impact on reading acquisition, and research has also shown that a foundation in phonemic awareness and phonics can positively affect other key elements of literacy, such as fluency, vocabulary development, and comprehension. The 5 Pillars of Literacy—also known as the Big 5 of Reading—remain widely accepted by researchers and educators as core elements of effective reading instruction.
Amira Learning uses the power of automated speech recognition and artificial intelligence technology to assess and report on students’ skills across key pillars of reading and to enable oral reading practice supported by a variety of micro-interventions tailored to each individual student’s specific needs. Each micro-intervention is a scaffold that helps an emerging reader improve skills that Amira Learning’s assessments have identified as needing more work toward mastery. In addition, student performance on Amira’s oral reading fluency assessment is linked to resource recommendations from HMH’s core English Language Arts program, HMH Into Reading®, to support teachers in providing instruction targeted to their students’ needs. This system connects assessment, reporting, instruction, and practice to help teachers understand the impact of their instruction and determine how to target instruction to students’ needs in an iterative, data-driven cycle (Pellegrino, 2014; Wiliam, 2014). This section describes the research underlying the essential elements of the Amira Learning pedagogy: assessment, reporting, differentiated instructional recommendations, and individual practice supported by micro-intervention scaffolds.
ORAL READING FLUENCY ASSESSMENT
Reading fluency is accurate, expressive reading at a rate appropriate for enabling comprehension. Oral reading fluency is a measure of the number of words a student can read aloud correctly and with natural ease per minute (Valencia et al., 2010). Measures of words correct per minute (wcpm)—also commonly referred to as running records—are used by literacy and language teachers across the United States to assess oral reading fluency in elementary school students (Armbruster, 2010; Hasbrouck & Tindal, 2006; Manzo, 2007). Fluency is an essential early literacy skill that has been described as a “bridge” between decoding and comprehension, enabling readers to shift their cognitive resources away from decoding and towards constructing meaning from text (Pikulski & Chard, 2005). Over time, the oral reading fluency assessment has become key to identifying at-risk students, placing students in remediation or special education, improving instructional programs, and predicting performance on high-stakes assessments (Klein & Jimerson, 2005; McGlinchey & Hixson, 2002).
How Amira Learning Aligns with the Research
Amira Learning’s Oral Reading Fluency assessment uses automated speech recognition and artificial intelligence technology to listen to children read aloud and analyze their oral reading accuracy and rate. Amira Learning was developed by scientists at Project LISTEN in conjunction with psychometricians, neuroscientists, and reading scientists to produce reliable and valid assessments of oral reading fluency. After a 5–7 minute oral reading fluency assessment, Amira Learning analyzes student reading, produces a running record of errors, and reports scores with actionable insights.
Early Identification. Research shows that early screening and detection is critical for students with reading difficulties. There is wide consensus among researchers and educators about the importance of administering screening tests as students first enter school and again at the beginning and middle of each year from kindergarten through Grade 3 (Gersten et al., 2008). Early and frequent screening using high-quality instruments that are efficient, reliable, and valid are needed to provide timely identification of students who might be at risk for reading failure, learning disabilities, and/or dyslexia (Washington, Compton, & McCardle, 2010). Repeated administrations of screening tests help schools track students’ progress and rate of growth, adjust instruction as needed, and provide additional services to prevent later problems (Gersten et al., 2008).
Prevention and Intensive Intervention. Petscher and colleagues (2019) state that early screening and intervention services are critical for students with undiagnosed literacy-related disabilities, including dyslexia. Effective prevention and early reading intervention services should focus on the literacy-related problems. This includes providing intervention to students who are not yet diagnosed with literacy-related disabilities, including dyslexia, as well as those students who are experiencing literacy-related difficulties for other underlying reasons (Shaywitz & Shaywitz, 2020). Students’ reading skills are developed and established in the early elementary years and are stable over time unless additional support and interventions are supplied to accelerate students’ literacy growth (Petscher et al., 2019; Torgesen, 2000). Longitudinal data suggest that reading interventions that begin prior to the third grade are more effective than those that begin later in students’ schooling (Juel, 1988; Torgesen et al., 2001). No matter the cause of the literacy issues (e.g., dyslexia, other learning disabilities, low oral language skills, etc.), early, systematic, and intensive intervention is the best solution to prevent long-term effects of reading difficulties over a period of the students’ schooling and lifespan (Connor et al., 2014).
How Amira Learning Aligns with the Research
Amira Learning’s Dyslexia Screener uses automated speech recognition and artificial intelligence to listen to students respond to a set of measures and analyzes their phonological awareness, alphabetic awareness, word reading, and rapid automatized naming (RAN) skills. Developed in conjunction with psychometricians, neuroscientists, and reading scientists, Amira Learning meets the universal screening criteria recommended by the International Dyslexia Association. With more than two decades of research supporting its effectiveness (including Project LISTEN, on which Amira was based), the content/technology built into Amira Learning’s Dyslexia Screener has demonstrated consistent and reliable results with strong predictive validity.
In 7–9 minutes, Amira Learning’s Dyslexia Screener delivers a reliable and valid assessment of dyslexia risk. Amira Learning can also screen multiple students at the same time, saving teachers valuable time for instruction and planning. Furthermore, there are multiple versions of the screener for each grade level so students can be screened multiple times each year. Amira Learning automatically generates a Dyslexia Risk report that helps teachers identify next steps for intervention and further evaluation.
Amira Learning’s Dyslexia Screener:
REPORTING AND RECOMMENDATIONS
Amira Learning automatically scores and records each student’s oral reading and/or responses to the Dyslexia Screener, and allows the teacher to choose among the following types of score reports. Instructional resource recommendations based on a student’s Oral Reading Fluency assessment performance can be found in the Diagnostic Report. Into Reading resources are recommended that support teachers in providing targeted instruction and/or practice for the skills that Amira identifies for each student.
The Reporting Dashboard provides data that can be used to inform instruction. Specifically, teachers can:
Scaffolding is the temporary assistance the teachers provide for the students in order to assist the students to complete a task or develop new understandings, so that they will later be able to complete similar tasks alone (Hammond, 2001). Hammond notes several essential features of scaffolding:
Scaffolding is also known as the gradual release of responsibility, where teachers initially take on most of the responsibility for learning but gradually transfer it to the learner as he or she becomes more skilled. A common form of scaffolded practice is the “I do, we do, you do” model, where the teacher first models how to complete a task (I do), then works on the task together with the students (we do), and finally allows the students to complete the task independently (you do) (Fisher & Frey, 2008; Fisher, 2003). The gradual release of responsibility model of instruction has been documented as an effective approach for improving literacy achievement (Fisher & Frey, 2008), reading comprehension (Lloyd, 2004), and literacy outcomes for English language learners (Kong & Pearson, 2003).
The practice of scaffolding is widespread in formal K-12 education systems and also in digital learning environments (Dalton & Rose, 2008). Research has demonstrated that embedding scaffolds such as vocabulary definitions, additional contextual information, main ideas of text, and reading strategy prompts supports comprehension of digital text (Anderson-Inman & Horney, 1998).
How Amira Learning Aligns with the Research
Amira Learning uses data obtained from its reading assessments to deliver scaffolded reading practice that is personalized based on each student’s specific needs. Amira Learning’s automated reading tutor delivers targeted instruction, practice, and feedback in all five key elements of early literacy: phonemic awareness, phonics, fluency, vocabulary, and comprehension. Amira Learning uses artificial intelligence technology to measure, define, and report each student’s learning progression in order to ensure that advanced skills are not introduced prior to acquisition of prerequisite skills.
Amira Learning uses the following metrics and scales to identify needs for scaffolded practice:
Amira Learning assesses skills each time a student uses the software, and does not introduce new skills before a student has mastered the prerequisite skills. Amira Learning uses the learning progression to recommend reading resources keyed to each student’s skills. Amira Learning has an extensive library of high-quality reading selections, and also allows schools and districts to upload their own reading selections. Amira Learning provides teachers with automatically generated score reports of each student’s progress along with actionable insights for instruction and remediation.
A cumulative approach to reading instruction is based on evidence from research studies conducted over decades and established on learning progressions theory. Learning progressions have been defined as empirically grounded and testable hypotheses about how students’ understanding of core concepts within a subject domain grow and become more sophisticated over time (Corcoran, Mosher, & Rogat, 2009). Skills follow a logical order of the language, and skills are organized with the easiest and most basic concepts and elements and progress methodically to more difficult concepts and elements from grade to grade. Cumulative means each step must be based on concepts previously learned. Cognitive science research has shown that learning is cumulative. Complex cognitive skills can be broken into simpler skills, which can in turn be broken into even simpler skills, and lower-level skills must be mastered before higher-level skills can be mastered (Gagne & Briggs, 1974).
How Amira Learning Aligns with the Research
Amira Learning takes a systematic, explicit, and cumulative approach to reading instruction. Based on the Simple View of Reading, Amira Learning’s multi-threaded learning progression spans the five key pillars of early literacy and reading instruction: Phonemic Awareness, Phonics, Fluency, Vocabulary, and Comprehension.
The essential design of Amira Learning’s multi-threaded learning progression is that skills are integrated by literacy thread or area. Instruction is systematic and cumulative in that within a thread, easier prerequisite skills are mastered before more difficult skills are introduced. Amira Learning’s diagnostic score reports provide data about each student’s mastery of the skills within a thread (intra-thread linkage). Within each thread, Amira Learning categorizes skills into a vertical stack based on a student’s level of mastery.
The vertical mastery stack serves to illustrate intra-thread linkage of literacy skills within a pillar, and also to present the key skills as a spectrum and highlight the skills currently within a given student’s Zone of Proximal Development (ZPD).
Amira Learning also links skills and mastery horizontally across the threads (inter-thread linkage) to show how multiple threads are woven together to form the two components of the Simple View of Reading—word recognition and language comprehension (Gough & Tunmer, 1986; Scarborough, 2001).
Amira Learning obtains frequent assessments of each student’s mastery of key skills across the multiple threads that make up each strand of literacy, and reports the data along with actionable insights to help the teacher plan targeted instruction. Via the AI avatar, Amira, the program delivers targeted scaffolded instruction in component skills like decoding, segmentation, blending, and pronunciation. What makes Amira Learning unique is its ability to respond to each student’s reading errors in the moment by providing explicit modeling and instruction that is tailored to the student’s needs.
Each scaffolded support within Amira Learning is a response to errors in the assessment phase and a means by which Amira, the AI avatar, guides student through the reading material at hand and tutors them to build critical foundational skills. Amira Learning offers three classes of interventions that differ in when Amira, the AI avatar, corrects errors and delivers feedback: at the moment a word is being (incorrectly) read, at the end of a sentence, and at the end of a page or passage. Amira Learning’s interventions are based on evidence from reading science. Therefore, this inventory of scaffolded support, also referred to as micro-interventions, is organized by the Five Pillars of Literacy—those skills identified as critical elements of effective early literacy instruction.
Effective reading instruction in the early grades focuses on helping students understand the role that phonemic awareness plays in learning to read and write. Phonemic awareness refers to the ability to identify and manipulate individual speech sounds in oral language (NICHD, 2000). A phoneme is the smallest unit of sound in a given language that can be recognized as being distinct from other sounds in the language. For example, the word cap has three phonemes (/k/, /a/, /p/), and the word clasp has five phonemes (/k/, /l/, /a/, /s/, /p/). Phonemic awareness is essential to reading because hearing the individual component sounds in words is key to matching them with alphabet letters when learning to decode.
The importance of phonemic awareness in learning to read has been well documented. The National Reading Panel reviewed decades worth of reading research and concluded that phonemic awareness and letter knowledge are the two best indicators of how well children will learn to read during the first two years of instruction. Recent research also shows that phonemic awareness is an essential precursor to reading, and that listening to and using language helps many though not all students gain this awareness prior to entering school (Brady, Braze, & Fowler, 2011).
How Amira Learning Aligns with the Research
Amira Learning provides the phonemic awareness activities listed in Table 5:
Effective reading instruction in the early grades focuses on helping students learn letter-sound correspondences. After learning to hear the sounds of speech, the next step for students is to learn phonics—the relationships between written letters (called graphemes) and the individual sounds they represent (phonemes). As these understandings fall into place, students begin to decode.
Initially, they may recognize familiar words on sight, but gradually they should apply what they know about letter-sound correspondences to decode words as they read and to encode words as they write (Foorman et al., 2016). Thus, in addition to learning letter-sound patterns, beginning readers must become fluent in decoding—the process of segmenting letter-sound patterns within words and blending them back together to access that word in their lexicon.
For some students, the transition from the understanding of how oral language functions to applying the same principles in understanding print requires patient, consistent teacher support. Once students know a few consonant and vowel sounds and their corresponding letters, they can start to sound out and blend them into words in isolation and in context. In this process, they must use their recognition of letter shapes, understand the order of letters in words, access the sounds of these letters, and put together the meanings of the words to create a basic understanding of the words on the page or screen (Adams, 1990; Cunningham & Allington, 2011).
As these understandings of the sounds of the letters and the written letters fall into place, students begin to decode. Initially, they may recognize familiar words by sight, but gradually they should apply what they know about letter-sound correspondences to decode words as they read and encode words as they write. The development of automatic word recognition depends on intact, proficient phoneme awareness, knowledge of sound-symbol correspondences, recognition of print patterns such as recurring letter sequences and syllable spellings, and recognition of meaningful parts of words (Moats, 2020).
Effective reading teachers also include instruction in syllable structure, which can help guide pronunciation of a written word, and morphology (knowledge of word parts like roots and affixes), which can also provide reliable information about pronunciation and meaning. Mastering advanced decoding skills like syllable structure and morphology can facilitate reading multisyllabic words. Effective reading instruction helps students master sound-symbol associations in two directions: visual to auditory (reading), and auditory to visual (spelling). Reading requires segmenting of whole words into the individual sounds, while spelling involves the blending of sounds and letters into whole words. As such, learning to spell reinforces learning to read; spelling and reading are the productive and receptive sides of the same coin.
Strong teachers teach these skills explicitly with detailed explanations, modeling, and practice (Strickland, 2011). In these ways, teachers demonstrate the utility of the sophisticated concepts and skills students are working to master. Students should also be encouraged to try the skills out themselves by reading simple text or beginning to write on their own. This mixing of explicit instruction and practice activities strengthens students’ understanding and gives them confidence as beginning literacy users. Students can also practice phonics skills by taking dictation from teachers; the resulting products give teachers valuable informal data about students’ understanding of letter-sound correspondences and of letter formation.
How Amira Learning Aligns with the Research
Amira Learning provides activities that focus on developing students’ grapheme-phoneme correspondence skills, decoding skills, recognition of high-frequency words, and knowledge of morphology (Table 6).
Fluency refers to the ability to read letters, sounds, words, sentences, and passages, both orally and silently, with speed, accuracy, and the appropriate expression (NELP, 2008). Fluency is a reading skill that acts as a bridge between decoding and comprehension (NICHD, 2000).
A key component of fluency is accuracy, the ability to read or pronounce the words in a text correctly. Findings from research show that fluent reading depends on accurate and automatic word recognition, which in turn requires mastery of phonemic awareness and letter naming (Rasinski et al., 2006).
The rate or speed at which words are read is an essential component of reading fluency. The ability to accurately and quickly recognize letters, spelling patterns, and whole words with automaticity and effortlessness is essential to reading comprehension (Adams, 1990). When students’ word identification becomes fast and accurate, they have freed up some “cognitive space” to draw on their broader knowledge of language and to comprehend what they are reading (Baker et al., 2017; Hoover & Gough, 1990).
Researchers at the LRCC found that that word recognition fluency—a measure that includes both accuracy and rate—significantly predicted reading comprehension of students in Grades 1–3 (LRCC, 2015). Additionally, the researchers found that the importance of rate increases as students’ literacy skills develop; accuracy is a stronger predictor of reading comprehension for first and second graders, but for third graders, measures of fluency that include rate predict reading scores better than accuracy scores alone (LRCC, 2015).
Prosody refers to the ability to read aloud with appropriate phrasing, intonation, and expression. Prosody also refers to the ways in which tone of voice and inflection convey meaning in oral language—for example, the way one expresses sarcasm or irony. Prosody is important because reading involves more than reading quickly and accurately—readers must also comprehend the meaning of text. Fluency is intricately linked to reading comprehension because strong readers demonstrate silent reading fluency as they recognize words and their meaning automatically and can attend primarily to making sense out of what they read (NICHD, 2000). Fluency—or lack thereof—may indicate to readers that they may have to go back to reread sections or to look up the meanings of some words.
According to Kuhn and colleagues (2010), prosody is separate from accuracy and rate in beginning readers: children cannot both read very quickly and with proper prosody at the same time. Research from cognitive psychology suggests that one of the functions of prosody is to help the reader retain an auditory sequence of sounds and words in working memory so that they can work to comprehend the meaning of text (Frazier et al., 2006; Swets et al., 2007). Taken together, these findings indicate the need to develop students’ prosody in addition to accuracy and rate.
As teachers help students to become fluent readers, they need to reassure them that fluency means reading with comprehension, not merely saying the words as quickly as possible. Teachers model this distinction in their oral reading by pausing to question the meaning of words, the implications of word choice, or other aspects of the texts they are reading.
How Amira Learning Aligns with the Research
Amira Learning provides fluency activities that help students focus on their rate and prosody of reading aloud connected text (Table 7).
From the very beginning, high-quality early literacy instruction must also include instruction and practice on vocabulary (Beck, McKeown, & Kucan, 2013; Cunningham & Stanovich, 1997; Foorman et al., 2016). The extent of students’ vocabularies varies widely when they enter school, often reflecting variety in home environments and prior experiences, such as differences between the language of home and of school or preschool attendance (Toub et al., 2018; Hart & Risley, 1995; Kieffer & Stahl, 2016). Teachers’ everyday conversations with students can minimize these differences and expand students’ oral vocabularies and concepts, in addition to their efforts to teach students academic language skills such as how to talk about books and about their own reading and writing (Foorman et al., 2016; Shanahan et al., 2010). Students’ vocabularies expand from repeated encounters with new words, both in the literacy block and in content-area instruction (Connor & Morrison, 2012); vocabularies also grow from listening, reading, and talking to others.
How Amira Learning Aligns with the Research
Amira Learning embeds vocabulary activities that help students understand the meaning, context, and usage of academic and content-specific vocabulary words (Table 8).
Spanish Supports for Vocabulary
In addition, Amira Learning supports Spanish-speaking English learners with Spanish supports throughout the software (Table 9).
Comprehension is the ultimate goal of learning to read, and even beginning readers benefit from instruction that introduces them to a variety of strategies to help them understand different kinds of texts and their text structures (Duke, 2000; Shanahan et al., 2010). Part of beginning comprehension instruction is teacher “externalizing” or modeling the comprehension strategies mature readers use automatically. The daily read-aloud period is an ideal means for this instruction—so long as teachers remember that merely reading aloud isn’t enough. Students need to be actively involved in asking and answering questions, making predictions, or explaining characters’ motivations or other actions in what they are hearing (Duke & Pearson, 2002; Reutzel et al., 2008; Shanahan et al., 2010). Researchers have found positive relationships between students’ reading growth and the extent to which they have engaged in “analytic talk” during the back-and-forth with teachers during read alouds (McGee & Schickendanz, 2007). This makes sense because the listening comprehension of young learners far surpasses their emerging reading comprehension skills.
Of course, this kind of instruction is most effective when teachers have access to high-quality children’s literature in a variety of genres and representing different cultural backgrounds and experiences. It is especially important that students experience high-quality informational books in addition to narrative literature representing different cultural backgrounds and experiences (Duke, 2000). One of the great advantages of introducing students to reading comprehension skills by giving them opportunities to read on their own in books at the right level is that the experience reinforces that the students themselves do indeed have the capacity to become successful readers (Sisk et al., 2018). Empirical studies have demonstrated that children’s independent reading provides a unique mechanism to increase reading fluency, academic vocabulary (Cunningham, 2005), and general world knowledge (Cunningham & Stanovich, 1998; Stanovich & Cunningham, 1993).
How Amira Learning Aligns with the Research
In addition to ensuring that students have mastered decoding and word recognition skills, Amira Learning provides comprehension support on all texts to ensure that students are understanding the passages they are reading (Table 10).
MOTIVATING ALL LEARNERS
Educators and researchers often distinguish between two types of motivation: intrinsic and extrinsic. Intrinsically motivated learners are those who are driven by a love for learning and desire for self-satisfaction, while extrinsically motivated learners are driven by a quest for external rewards like praise, high scores, good grades, and money (Corpus et al., 2009). Research has shown that both forms of motivation are related to learning, with intrinsic motivation having stronger effects on learning and achievement. A longitudinal study of middle school students found that fifth-graders’ intrinsic motivation, perceived competence, and engagement with school were significant predictors of their reading achievement in Grade 8 (Froiland & Oros, 2013). Research on motivation and mindset demonstrates that how teachers deliver praise has an effect on students’ beliefs about their own intelligence (Dweck, 2007). Students who are praised for their effort and grit rather than their talent or ability are more likely to develop malleable growth mindsets, resilience to setbacks, and increased motivation to learn (Dweck, 2007).
How Amira Learning Aligns with the Research
Amira Learning was designed to be a patient and non-threatening program. Within the comfort zone that the software provides, students are motivated by effective praise, targeted feedback, entertaining and high-interest content, algorithms that recommend content based on student interests, having agency in choosing what to read (at an appropriate level), and completing a story.
Amira Learning is designed to build motivation, foster a sense of agency, and encourage grit and stamina in young readers. The software is centered on the reading cycle—selection, practice, skill building, reward, and progress monitoring. Amira Learning is aligned with the considerable research that shows that providing students with choice is effective in increasing motivation. On entry, each student is presented with a set of appropriately leveled reading resources selected by Amira Learning’s AI technology to build the skills within the student’s ZPD and allowed to choose which text to work with.
As a student reads with Amira, he or she receives instantaneous feedback. This breakthrough aspect of the Amira Learning software prevents lack of immediacy from sapping motivation and interest. In addition to immediate formative feedback, Amira Learning also provides summative reports of student progress upon completion. Amira Learning’s progress reports allow a student to view his or her latest performance scores and also their progress over time.
Additionally, Amira Learning is aligned to research on effective use of praise. Amira follows evidence-based best practices in praising students for effort, determination, and persistence rather than success or achievement. Amira Learning is designed to deliver praise whenever students show that they are trying to exercise and extend their skills.
TEACHING EXCEPTIONAL LEARNERS
Students with Disabilities
Early and frequent screening of students in Kindergarten to Grade 3 provides the first means of identifying students with disabilities and students with dyslexia (Gersten et al., 2008). Results from screening tests may suggest that more focused diagnostic testing is advisable to pinpoint the causes of students’ potential struggles. Data from such testing that indicates students are at risk for reading failure should set into motion development of a Response to Intervention (RTI) plan and, if needed, further evaluation and the development of an individualized education program (IEP). To maximize success for these students, classroom teachers and specialists need to work together to ensure that the plan is followed and the interventions are successful. Students’ RTI plans and IEPs most likely provide guidance for the Tier 1 instruction.
Literacy scaffolding is vital for students with disabilities, and computer-based literacy instruction offers many ways to provide necessary supports for students with disabilities. Research has shown that assistive technology software providing text-to-speech features along with built-in supports improves access to learning and also leads to large performance gains for students with visual impairments and learning disabilities (Elkind & Elkind, 2007; Izzo et al., 2009). Researchers have discovered that compared to traditional static text, supported electronic text with interactive multimedia links and resources has been helpful to readers who struggle to acquire word meanings (Anderson-Inman & Reinking, 1998; Anderson-Inman, 2009).
Students with Dyslexia
Dyslexia is a specific learning disability that is neurobiological in origin that is characterized by an “unexpected difficulty in reading for an individual who has the intelligence to be a much better reader, most commonly caused by a difficulty in the phonological process, which affects the ability of an individual to speak, read, and spell” (Shaywitz & Shaywitz, 2020, p.100). Secondary consequences may include problems in reading comprehension and reduced reading experience that can impede growth of vocabulary and background knowledge (IDA, 2020).
Early identification, remediation, and providing accommodations such as assistive technology where necessary are critical for minimizing these secondary consequences and others such as the detrimental effects of experiencing repeated failure. Developing a dislike for reading can make problems worse if students avoid reading and thereby fall further behind.
Over the past couple of decades, the development of methods of detection and interventions for dyslexia have increased, and many have incorporated the use of technology. Conventional dyslexia detection processes are now augmented with computational intelligence techniques (Jain et al., 2009; Gaggi et al., 2012; Perera et al., 2016).
Research indicates that students with dyslexia perform worse in reading irregular and nonsense words compared to regular words, suggesting that impairments in decoding are characteristic of dyslexia (Ziegler et al., 2008). Recent research has highlighted the importance of rapid naming skills in fluent reading. The ability to quickly and automatically process, identify, and name familiar text and objects is related to reading (Georgiou, 2013), and this skill is impaired in students with dyslexia (Jones et al., 2010).
Moreover, students who struggle with reading may lack the “reading stamina” needed during a literacy block that requires independent work in addition to working with teachers and students. Students with reading difficulties need extra practice, extra time, and books aligned with their proficiency that engage their interests.
How Amira Learning Aligns with the Research
Amira Learning provides both the Dyslexia Screener for early detection and identification of students who are at risk for reading difficulties and subsequent personalized practice that meet each students’ unique needs. Amira Learning integrates assistive technology supports that allow learners with visual and auditory disabilities to access text. Amira Learning uses the power of automated speech recognition and artificial intelligence to listen to students read aloud and analyze their phonological awareness, alphabetic awareness, word reading, and rapid automatized naming skills, allowing frequent and early screening for dyslexia. Because Amira Learning is designed to adapt and personalize practice, the software quickly identifies striving readers and optimizes interactions for these students.
The best practices included in the report “Teaching Academic Content and Literacy to English Learners in Elementary and Middle School” published by the Institute of Education Sciences outlines four recommendations:
English learners may have difficulty mapping standard English phonology, conventions, and syntax due to differences between English and their primary language.
The research on effective instruction for English learners points to three important principles: 1) generally effective practices are likely to be effective with English learners; 2) English learners require additional instructional supports; and 3) the home language can be used to promote academic development. Additionally, English learners need plenty of opportunities to develop proficiency in English (Goldenberg, 2013).
Teachers can accelerate the language proficiency of English learners by explicitly teaching the conventions, vocabulary, and structures of academic language in specific domains (Dutro & Kinsella, 2010). Many English learners need to acquire new phonemes or orthographic patterns as well as new matches between phonological segments and orthographic patterns (Durgunoglu, Nagy, & Hancin-Bhatt, 1993). Additionally, teaching vocabulary as it is used in specific genres prepares English learners to succeed with academic writing tasks (Schleppegrell, 1998).
How Amira Learning Aligns with the Research
While a student reads, Amira recognizes the subtleties of various dialects, speech deficits, and accents to deliver results free of bias. The effectiveness of Amira Learning for English learners has been illustrated in experimental studies by Project LISTEN researchers and by independent researchers at the University of British Columbia and DePaul University. Results from the studies have demonstrated that English learners who used Amira Learning made significant gains in reading scores and outgained students in the control conditions (e.g., Poulsen et al., 2007; Reeder et al., 2007; Reeder et al., 2008; Reeder et al., 2015).
Amira Learning’s success with English learners is grounded in a set of accommodations and adjustments specifically aimed at the special needs and challenges of these students.
Amira in Spanish
To assist students coming from homes where Spanish is primarily spoken, Amira in Spanish delivers tutoring in Spanish to provide first language support. Although the student is reading in and learning English, the AI avatar, Amira, interacts with the student in Spanish.
Amira in Spanish operates precisely the same as the English version, except that the AI avatar, Amira, delivers directions, micro-interventions, and feedback in Spanish. In addition, student assessment data from Amira in Spanish populates the same reports as the standard Amira Learning data.
Whether a student works with Amira Learning in English or Spanish, the software delivers a range of micro-interventions specialized to help English learners. These targeted tutoring techniques include:
Amira Learning does much of the heavy lifting for teachers by delivering assessments, generating score reports, and proctoring students. HMH provides a continuum of professional learning to not only support a successful Amira implementation but help teachers use Amira data to strengthen teaching and learning. Through strategic planning, live online and on-demand professional learning courses, and coaching HMH partners with districts and schools to provide implementation support grounded in agency, collaboration, and teacher success.
Getting Started with Amira Learning
A Getting Started live online 2-hour session provides the hows and whys of Amira through exploration and collaborative experiences. Teachers will spend time digging into the program to gain a real-world application of Amira and how best to use it in their classroom. The goal is to build deeper understanding and confidence to begin implementing Amira.
Continue Collaboration with HMH Professional Learning Live Online Courses and Blended Coaching
To strengthen teacher practices and maximize their investment in Amira, HMH provides live online professional learning courses aligned to district’s strategic literacy plan. We partner with districts to design a personalized live online course experience to cultivate the next generation of critical thinkers through reading and writing. Each live online course experience includes one hour of consultative planning and six 1-hour shared learning sessions that can be delivered over time to meet your needs.
Blended coaching continues to foster collaboration and provides teachers with personalized support focused on lesson design, instructional practices, content, and data-driven decision-making to promote continuous improvement over time. HMH literacy coaches build strong relationships with teachers by modeling high-impact instructional strategies, answering program and practice questions, leading grade-level program sessions centered on evidence of student learning, and helping teachers select, monitor, and achieve goals. The online and blended coaching experience includes access to the HMH Coaching Studio, which provides access to additional resources and interactive collaboration such as:
HMH Teacher’s Corner™, our easy-to-use, approachable professional learning site located on the Ed platform, offers program and lesson-integrated support and access to a constantly growing library of resources. Teacher’s Corner resources range from authentic classroom videos to tips from other teachers, plus content and support from experienced HMH professional coaches. The fresh content, clean format, and friendly faces of peer educators create a welcome space for teachers to learn and grow at their own pace. Please visit /programs/teachers-corner for a quick video tour.
The basic pillars of literacy instruction used in Amira Learning have long been shown to be effective. Drawing on decades of research in computer science, cognitive psychology, and artificial intelligence, Amira Learning delivers targeted instruction, practice, assessment, and feedback in phonemic awareness, phonics, fluency, vocabulary, and comprehension. This unique approach is highly effective with students of varying ability levels and allows students to gain and retain critical literacy skills essential for lifelong learning.
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