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Using the LENA in teacher training: Promoting student involvement through automated feedback
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As part of professional development training for mathematic teachers, we used a speech recognition recorder (Language ENvironment Analysis, LENA) to create an automated teacher feedback system to help teachers monitor and limit the time they talk and to increase students’ active participation in mathematics lessons. Teachers got feedback with a 12h turnaround which allowed them to see how much they and their students talked on a daily basis. In this study, we wanted to know whether a) the system indicated change in the talk pattern, b) whether more complex indicators can be developed that are useful for professional development. Based on a pilot study we were able to implement an automated feedback of quality discussion episodes which was effective in increasing the amount of teacher-student discussion.
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... For example, from a PCK perspective as a professional lens, an experienced mathematics teacher will take note if a student picks up an initial idea from another student and elaborates on it without the teacher's prompting, which is considered an important feature of quality instruction according to NCTM (NCTM, 2000). It is worth mentioning that this is a feature of quality instruction that can be measured using computer algorithms (Wang et al., 2014) and defines a skill that improves with specific training (Wang et al., 2013). ...
... For instance, beginner learners (Bosch & D'Mello, 2017) were taught a difficult subject in Computer Science and experienced fear and confusion due to its complexity, whereas in another case (Arroyo et al., 2009), various sensors were used in the classroom to record body actions during the teaching of mathematics, revealing that anxiety was evident in the order of 60%. In addition, Wang et al. (2013) used voice recognition recorders to help educators limit speaker time and encourage the active participation of learners. The results were spectacular as increased interactivity was observed among learners and educators. ...
Learning Analytics (LA) are constantly evolving in the analysis and representation of data related to learners and educators to improve the learning and education process. Data obtained by sensors or questionnaires are processed employing new technologies. The emotional experiences of learners constitute a significant factor in the assimilation of knowledge about the learning process. The emerging technology of Emotional Learning Analytics (ELA) is increasingly being considered in educational settings. In this work, the significance and contribution of ELA in educational data processing are discussed as an integral part of designing and implementing computational models reflecting the learning process. In addition, a comprehensive overview of different methods and techniques for both LA and ELA is provided, while a conceptual model of ELA is proposed in parallel. Moreover, advantages, disadvantages, and challenges are highlighted. The final focus of the chapter is on ethical issues and future research directions.
... Advances in Artificial Intelligence (AI) and Natural Language Processing (NLP) have great potential for analyzing instructional discourse and providing substantive feedback to support teacher learning. Possible applications include identifying different types of classroom activities [13,43,44,52] and providing automated feedback on various teacher discourse moves, such as moves designed to (a) guide discussion and ensure students' participation [20,50], (b) ask authentic questions; namely, questions for which the answers are not presupposed by the teacher [1,22] or (c) restate and use student ideas [10]. Traditional approaches to automated analysis of classroom discourse typically employ supervised machine learning (ML) methods combined with manual feature engineering and human expert annotation of collected data according to evaluation rubrics. ...
... These systems are capable of providing personalized indications to student teachers through automatic analysis of teacher speech or combining feedback and reflection within scenario-based learning activities. In the study of Wang, Miller and Cortina (2013), a speech recognition recorder, called LENA (Language ENvironment Analysis), was used to create an automated teacher feedback system to help teachers monitoring and limiting the time they talk, in order to increase students' active participation in lessons. In Bardach et al (2021), an online scenario-based learning was used to prepare student teachers for the classroom readiness and self-efficacy, providing feedback on authentic learning experiences that teachers can solve in everyday practice. ...
This paper aims to illustrate an automated system developed to give formative and personalized feedback to teachers in training. It is an expert system (Paviotti, Rossi & Zarka, 2012) that uses concrete examples, cases and scenarios to guide the engaged learners (Leake, 1996). In this regard, this system is able to create questionnaires, deliver them, collect and analyze data, send feedback to the participants to provide information about their beliefs and behaviors about teaching and learning processes. Far from constituting an assessment of teaching practices, the automated feedback demonstrates its usefulness in identifying teachers’ mindframes at an early stage, so as to be able to implement more specific and personalized training. This allows its application to be extended to further training areas as well as constituting an effective approach for need analysis and a preparatory action for numerous training activities (guided discussion with experts, observation on practice, modeling, etc.).
With the popularity of online one-to-one tutoring, there are emerging concerns about the quality and effectiveness of this kind of tutoring. Although there are some evaluation methods available, they are heavily relied on manual coding by experts, which is too costly. Therefore, using machine learning to predict instruction quality automatically is an effective way to reduce human costs. Three classification methods are analyzed in this paper: 1) Random forest algorithm with human engineered descriptive features; 2) LSTM algorithm with acoustic features generated by openSMILE toolkit; 3) CNN algorithm with Mel spectrogram of the audio. The results show that the three approaches can complete the prediction task well, with the second approach exhibiting the best accuracy. The importance of the features in these classification models is analyzed according to eXplainable Artificial Intelligence techniques (i.e. XAI) and statistical feature analysis methods. In this way, key indicators of high-quality tutoring are identified. This study demonstrated the usefulness of XAI techniques in understanding why some tutoring sessions are of good quality and others are not. The results can be potentially used to guide the improvement of online one-to-one tutoring in the future.
Prior studies have shown the importance of classroom dialogue in academic performance, through which knowledge construction and social interaction among students take place. However, most of them were based on small scale or qualitative data, and few has explored the availability and potential of big data collected from online classrooms. To address this issue, this paper analyzes dialogues in live classrooms of a large online learning platform in China based on natural language processing techniques. The features of interactive types and emotional expression are extracted from classroom dialogues. We then develop neural network models based on these features to predict high- and low-academic performing students, and employ interpretable AI (artificial intelligence) techniques to determine the most important predictors in the prediction models. In both STEM (science, technology, engineering, mathematics) and non-STEM courses, it is found that high-performing students consistently exhibit more positive emotion, cognition and off-topic dialogues in all stages of the lesson than low-performing students. However, while the metacognitive dialogue illustrates its importance in non-STEM courses, this effect cannot be found in STEM courses. While high-performing students in non-STEM courses show negative emotion in the last stage of lessons, STEM students show positive emotion.
Metacognition offers an up-to-date compendium of major scientific issues involved in metacognition. The twelve original contributions provide a concise statement of theoretical and empirical research on self-reflective processes or knowing about what we know.
Self-reflective processes are often thought to be central to what we mean by consciousness and the personal self. Without such processes, one would presumably respond to stimuli in an automatized and environmentally bound manner—that is, without the characteristic patterns of behavior and introspection that are manifested as plans, strategies, reflections, self-control, self-monitoring, and intelligence.
Bradford Books imprint
This study illuminates claims that teachers' mathematical knowledge plays an important role in their teaching of this subject matter. In particular, we focus on teachers' mathematical knowledge for teaching (MKT), which includes both the mathematical knowledge that is common to individuals working in diverse professions and the mathematical knowledge that is specialized to teaching. We use a series of five case studies and associated quantitative data to detail how MKT is associated with the mathematical quality of instruction. Although there is a significant, strong, and positive association between levels of MKT and the mathematical quality of instruction, we also find that there are a number of important factors that mediate this relationship, either supporting or hindering teachers' use of knowledge in practice.
merica's students are tested regularly, and the results serve as justification for closing schools, firing principals, awarding merits, and focusing professional development and curriculum reform. How- ever, little attention is paid to measuring the opportunities to learn that teachers create in classrooms—the quantity and quality of classroom instruction and relationships be- tween teachers and students. Nearly all state certification standards for "highly qualified teachers" focus on degree status (1, 2). However, despite evidence that teachers' instructional practices and relationships with students account for a substantial portion of the "added value" derived from attending school (3-5), observations of classroom experiences for large samples of students and teachers are limited. The Study Here, we describe results from a study of ele- mentary school classroom experiences for more than 1000 American children recruited at birth from 10 U.S. sites and enrolled in more than 2500 classrooms in more than 1000 elementary schools and 400 school dis- tricts. Our investigation approximates an epi- demiological study of opportunities to learn in U.S. classrooms, although the sample was not nationally representative. Of teachers observed, 90% were credentialed by their state; all had a bachelor's degree and 44% had a master's degree. These teachers meet many states' standards for highly qualified elemen- tary school teachers (1). Observation of classrooms complements student test performance as a measure of quality in the educational system and can document opportunities to learn that are reflected in students' performances on tests. We observed first, third, and fifth grades. First, we coded the presence of 44 behav- ioral events for 80 one-minute intervals. In a given school day, intervals were clustered into eight 10-minute cycles of coding, in which 30-second periods of observation alternated with 30-second periods of recording. Next, we rated nine dimensions of the quality of the emo- tional and instruc- tional climate on the basis of 20 minutes of observation, again across eight cycles during the day. We used these codes and ratings to assess opportunities to learn (6, 7).
Investigated the relationship between specific classroom behaviors and critical thinking. Four indicants of student involvement were measured using a modified version of N. A. Flanders's (1970) interaction analysis system: student participation, peer-to-peer interaction, faculty questions, and faculty encouragement and use of student ideas. Critical thinking was assessed by use of the Watson-Glaser Critical Thinking Appraisal and A. Chickering's (1972) behavioral self-report index. 12 college classrooms ( N = 138), equally divided among disciplines, were studied using canonical correlation, analysis of variance, and univariate analyses. Student participation, encouragement, and peer-to-peer interaction consistently emerged as being significantly and positively related to critical thinking. The results, though more suggestive than definitive, have significant implications for future research methodology and for faculty development efforts. (43 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
After twenty-three years of teaching, I stepped out of the classroom and into the world of education research. As part of a team of researchers comparing mathematics teaching and learning in the United States and China, I spent many hours watching videotaped mathematics lessons from fourth- and fifth-grade classrooms in both countries. It was fascinating. (To be honest, it was luxurious, since I was not also trying to grade spelling tests while I watched.) After I got past my initial reactions to the foreign setting—including bare walls, desks in rows, and over forty students per class—more substantive features of the differences between Chinese classrooms and what I was accustomed to seeing in U.S. classrooms began to capture my attention.
To investigate relationships between teaching and learning mathematics, the six second-grade classrooms in one school were observed regularly during the 12 weeks of instruction on place value and multidigit addition and subtraction. Two classrooms implemented an alternative to the more conventional textbook approach. The alternative approach emphasized constructing relationships between place value and computation strategies rather than practicing prescribed procedures. Students were assessed at the beginning and the end of the year on place value understanding, routine computation, and novel computation. Students in the alternative classrooms, compared with their more traditionally taught peers, received fewer problems and spent more time with each problem, were asked more questions requesting them to describe and explain alternative strategies, talked more using longer responses, and showed higher levels of performance or gained more by the end of the year on most types of items. The results suggest that relationships between teaching and learning are a function of the instructional environment; different relationships emerged in the alternative classrooms than those that have been reported for more traditional classrooms.
This study analyzed 1-4-year-old children's attention to television as a function of age, sex, and the presence or absence of a number of relatively simple auditory and visual characteristics of a TV program. Children's attention and attributes of the "Sesame Street" program were rated and stored in a computer so that continuous information about the TV program could be related to continuous information about attention. There was an increase with age in attention to the TV, and attention was elevated in the presence of some attributes and depressed in the presence of others. Many of the attribute effects interacted with age, but very few interacted with the sex of the child.
