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DEBE feedback for large lecture classroom analytics
Abstract
Learning Analytics (LA) research has demonstrated the potential of LA in detecting and monitoring cognitive-affective parameters and improving student success. But most of it has been applied to online and computerized learning environments whereas physical classrooms have largely remained outside the scope of such research. This paper attempts to bridge that gap by proposing a student feedback model in which they report on the difficult/easy and engaging/boring aspects of their lecture. We outline the pedagogical affordances of an aggregated time-series of such data and discuss it within the context of LA research.
... CVT provides a detailed overview of the factors related to the optimal academic development, of the concerned students, over a relatively long period . On the other hand, the education-specific emotions are more dynamic and therefore require to be monitored over a relatively shorter period (Mitra & Chavan, 2019;Simonton & Garn, 2019). Pekrun's theory (CVT) has been used to show the relation between positive and negative emotions and the overall achievement (Pekrun, Frenzel, Goetz, & Perry, 2007), and the dynamics of students' emotion has been explained using the cognitive disequilibrium theory (CDT) . ...
... CVT explains how and why the students' emotions contribute to academic and nonacademic outcomes Simonton & Garn, 2020). It provides a comprehensive framework for investigating the relationship between students' emotional antecedents and performance-based outcomes (Mitra & Chavan, 2019). There are three dimensions in the taxonomy that describes the achievement emotions: object focus (Activity versus Outcome, whether to focus on the process or the outcome), activation (Activating versus Deactivating), and valance (Positive versus Negative). ...
Educational research has used the information extracted from facial expressions to explain learning performance in various educational settings like collaborative learning. Leveraging this, we extracted the emotions based upon two different theoretical frameworks from videos with children aged 13–16 while collaborating to create games using Scratch. The two sets of emotions are based on the control value theory (happiness, sadness, anger, surprise) and the education-specific expressions (frustration, boredom, confusion, delight). We computed the groups’ objective performance, which was calculated based on their created artifacts. We divided them into high and low performance and compared them based on individual emotions’ duration and the transitions among the emotions. We also used the subjective indication of their perceived performance from a self-reported questionnaire, divided them into another performance category, and did a similar analysis with the objective performance. Results show that the objective performance is better explained by the education-specific emotions and the negative valance emotions from the control value theory-based emotions. On the other hand, subjective performance is better explained by the control value theory based on emotions. Based on the results, we suggest implications both for the instructors and students.
... The digital native [46] generation is far better at multitasking and is getting distracted with their devices any which way. Hence, it has been suggested that such distraction is not only manageable by this generation [47] but can also be source of 'meaningful distraction' whereby they learn to focus on how much they are learning [33]. ...
... Or "Is it difficult for me?" It is also an indication of how providing such feedback is capable of scaffolding students for metacognition, as suggested in [33]. ...
We have developed a web-based feedback system that students can use to report difficult, easy, engaging, and boring sections of a lecture in real-time. Such feedback can identify potentially problematic lecture content, track cognitive-affective dynamics in a classroom, and assist instructors in retrospective self-evaluation. We use a mixed-method approach within a design-based research (DBR) framework. In this paper, we discuss initial development and implementation of the feedback system, which constitutes the first cycle of DBR in the project. We discuss potential use-cases of such data and follow the DBR framework to identify strengths and weaknesses in the current prototype and its implementation. Based on such an analysis we come up with a list of modifications for the next cycle of DBR.
... However, between S3 and S4, engagement does not keep tanking and actually picks up again in S4, indicating that after the class reset its expectation between S1/S2 and S3 type of content, they re-engaged even when difficulty increased from S3 to S4. We hypothesized several such dynamics in Mitra and Chavan (2019). For example, had engagement dropped midway through S4 and not between S2 and S3, it could have indicated cognitive overload, where students started zoning out when the material became too difficult. ...
The use of online video lectures in universities, primarily for content delivery and learning, is on the rise. Instructors’ ability to recognize and understand student learning experiences with online video lectures, identify particularly difficult or disengaging content and thereby assess overall lecture quality can inform their instructional practice related to such lectures. This paper introduces Tcherly, a teacher-facing dashboard that presents class-level aggregated time series data on students’ self-reported cognitive-affective states they experienced during a lecture. Instructors can use the dashboard to evaluate and improve their instructional practice related to video lectures. We report the detailed iterative prototyping design process of the Tcherly Dashboard involving two stakeholders (instructors and designers) that informed various design decisions of the dashboard, and also provide usability and usefulness data. We demonstrate, with real-life examples of Tcherly Dashboard use generated by the researchers based on data collected from six courses and 11 lectures, how the dashboard can assist instructors in understanding their students’ learning experiences and evaluating the associated instructional materials.
... The students are given an interface where they can reflect on their cognitive-affective states and provide feedback at any time during the lecture just by clicking one of the four buttons ( Figure 2). The individual feedback from students can then be aggregated to reveal engaging and difficult segments of the lecture at high temporal granularity (Figure 3; see also Chavan, Gupta, and Mitra 2018;Chavan and Mitra 2019;Mitra and Chavan 2019). When combined with retrospective open-ended interviews to reveal the reasons underlying the DEBE feedback microscale perceptions about the lectureat the level of slides, figures or even bullet points on any slidecan be unearthed. ...
Student and teacher perceptions of course content are critical components of the teaching and learning process. Studies that have investigated such perceptions offer conflicting evidence of both convergence and divergence between the perceptions of students and teacher. These studies used data modalities such as interviews and surveys, which have different granularities of information in them. We analysed student and teacher perceptions of difficulty in a mechanical engineering lecture with multiple data modalities capable of capturing perceptions at different granularities. Our analysis revealed a multiscale nature of perceptions, wherein the students’ and their teacher’s perceptions can appear to both converge and diverge depending on the scale of interpretation, which is a function of the modality and granularity of data used in the study. At macroscale, the students and the teacher agreed about difficult sections of the lecture and the underlying reasons. However, their reasoning diverged when probed at finer scales. Furthermore, we note that convergence observed between the perceptions of the teacher and students at a coarse scale can sometimes hide differences in finer scale perceptions. We discuss the implications of these results for the interpretation of results from past studies and for practitioners.
... The DEBE framework has been recently proposed as a systemized way to collect continuous, fine-grained feedback from students on their levels of perceived difficulty (and affective states) during a lecture (Mitra & Chavan, 2019). We use student-self reports in the form of DEBE feedback to train an EEG machine learning algorithm that can be used to predict levels of perceived difficulty when interacting with a video lecture. ...
This study presents an approach to predict learner's perceived difficulty using features extracted from electroencephalography (EEG) data. We demonstrate how EEG signals can be used effectively to estimate learner's perceived difficulty of learning content. Student self-reports of perceived difficulty and EEG data were gathered from 9 participants who watched a video lecture. A machine learning model with random forest classifier achieved a maximum accuracy of 75.24% in estimating perceived difficulty. Furthermore, the model predicted the difficulty level of the entire video lecture for individuals fairly well. Our results have implications for intelligent tutoring systems which aim at providing the learner with an adaptive and personalized learning environment.
... LA/EDM is still exploring how to address challenges in providing highquality feedback to big student groups in higher education and propose innovative patterns in which feedback is equally scalable and useful. The authors in [96] present four central variables: Difficulty, Easy, Boring, and Engaging (DEBE), a novel feedback approach to inform teachers about their learning theories and advance old ones in order to reflect whether the students found the lecture to be Difficult/Easy and Boring/Engaging. This DEBE feedback approach highlights the pedagogical potential of datasets through the learning analytics tool. ...
With digital revolution expansion and a rapid change in the technologies, educational data is increasing at a swift pace. Learning analytics (LA) turns out to be a powerful tool for improving learning and teaching practices. Learning analytics uncover hidden patterns, correlation, and other insights about learners and educators in educational big data that leads them to stay agile, better outcomes, and employability. This literature review aims to categorize such measures of data-driven improvement. A comprehensive review of Learning Analytics (LA) and Educational Data Mining (EDM) and significant techniques in higher education was conducted. Analysis of the research questions, methodology, techniques, learning environment, associated projects, and findings of various published papers is done and is accordingly categorized. Analysis of various reviews on the development and growth of LA in higher education in various countries is also done. The results provide a comprehensive background for understanding current knowledge on LA and EDM and its impact on both learner and instructor in the various learning environment. The results showed that in HEIs, where LA has been implemented aimed at better assessing and predicting learner’s performance. It has also helped in monitoring and motivating them, discovering undesirable learning behaviors and their emotional states, help educators and administrators to unlock big data potentials, and making quicker data-driven decisions. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
The use of Experience Sampling Methods (ESM) to assess students’ experiences, motivation, and emotions by sending signals to students at random or fixed time points has grown due to recent technological advances. Such methods offer several advantages, such as capturing the construct in the moment (i.e., when the events are fresh on respondents’ minds) or providing a better understanding of the temporal and dynamic nature of the construct, and are often considered to be more valid than retrospective self-reports. This article investigates the validity and reliability of a variant of the ESM, the DEBE (an acronym for difficult, easy, boring and engaging, and pronounced ‘Debbie’) feedback, which captures student-driven (as and when the student wants to report) momentary self-reports of cognitive-affective states during a lecture. The DEBE feedback is collected through four buttons on mobile phones/laptops used by students. We collected DEBE feedback from several video lectures (N = 722, 8 lectures) in different courses and examined the threats to validity and reliability. Our analysis revealed variables such as student motivation, learning strategies, academic performance, and prior knowledge did not affect the feedback-giving behavior. Monte Carlo simulations showed that for a class size of 50 to 120, on average, 30 students can provide representative and actionable feedback, and the feedback was tolerant up to 20% of the students giving erroneous or biased feedback. The article discusses in detail the aforementioned and other validity and reliability threats that need to be considered when working with such data. These findings, although specific to the DEBE feedback, are intended to supplement the momentary self-report literature, and the study is expected to provide a roadmap for establishing validity and reliability of such novel data types.
When in front of a classroom, a skilled teacher can read the room, identifying when students are engaged, frustrated, distracted, etc. In recent years we have seen significant changes in the traditional classroom, with virtual classes becoming a normal learning environment. Reasons for this change are the increased popularity of Massive Open Online Courses (MOOCs) and the disruptions imposed by the ongoing COVID-19 pandemic. However, it is difficult for teachers to read the room in these virtual classrooms, and researchers have begun to look at using sensors to provide feedback to help inform teaching practices. The study presented here sought to ground classroom sensor data in the form of electrodermal activities (EDA) captured using a wrist-worn sensing platform (Empatica E4), with observations about students' emotional engagement in the class. We collected a dataset from eleven students over eight lectures in college-level computer science classes. We trained human annotators who provided ground truth information about student engagement based on in-class observations. Inspired by related work in the field, we implemented an automated data analysis framework, which we used to explore momentary assessments of student engagement in classrooms. Our findings surprised us because we found no significant correlation between the sensor data and our trained observers' data. In this paper, we present our study and framework for automated engagement assessment, and report on our findings that indicate some of the challenges in deploying current technology for real-world, automated momentary assessment of student engagement in the classroom. We offer reflections on our findings and discuss ways forward toward an automated reading the room approach.
Despite the best efforts of educators to introduce active learning techniques, classrooms remain largely didactic in nature. We have developed a unique audience response system to improve pedagogy in such large-lecture didactic classrooms. A pilot study with ten participants was conducted to assess the utility of such voluntary student feedback in improving pedagogy. The web-based feedback tool lets students voluntarily indicate their perception of the lecture on four key parameters-difficult, easy, boring and engaging in real-time. We discuss the potential of such feedback in refining pedagogy for didactic classrooms. We also discuss the usability of such a feedback system as revealed by a user perception study.
Monitoring classrooms using cameras is a non-invasive approach of digitizing students’ behaviour. Understanding students’ attention span and what type of behaviours may indicate a lack of attention is fundamental for understanding and consequently improving the dynamics of a lecture. Recent studies show useful information regarding classrooms and their students’ behaviour throughout the lecture. In this paper we start by presenting an overview about the state of the art on this topic, presenting what we consider to be the most robust and efficient Computer Vision techniques for monitoring classrooms. After the analysis of relevant state of the art, we propose an agent that is theoretically capable of tracking the students’ attention and output that data. The main goal of this paper is to contribute to the development of an autonomous agent able to provide information to both teachers and students and we present preliminary results on this topic. We believe this autonomous agent features the best solution for monitoring classrooms since it uses the most suited state of the art approaches for each individual role.
Despite a narrative that sees learning analytics (LA) as a field that aims to enhance student learning, few student-facing solutions have emerged. This can make it difficult for educators to imagine how data can be used in the classroom, and in turn diminishes the promise of LA as an enabler for encouraging important skills such as sense-making, metacognition, and reflection. We propose two learning design patterns that will help educators to incorporate LA into their teaching protocols: do-analyse-change-reflect, and active learning squared. We discuss these patterns with reference to a case study utilising the Connected Learning Analytics (CLA) toolkit, in three trials run over a period of 18 months. The results demonstrate that student-facing learning analytics is not just a future possibility, but an area that is ripe for further development.
We present our efforts towards building an observational system for measuring classroom activity. The goal is to explore visual cues which can be acquired with a system of video-cameras and automatically processed to enrich the teachers' perception of their audience. The paper will give a brief overview of our methodology, explored features and current findings.
Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis in the context of a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums, in a MOOC on educational data mining. The findings indicate that a mix of click-stream data and NLP indices can predict with substantial accuracy (78%) whether students complete the MOOC. This predictive power suggests that student interaction data and language data within a MOOC can help us both to understand student retention in MOOCs and to develop automated signals of student success.
Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g., accurate metacognitive judgments followed by the selection of effective learning strategies) and external (e.g., re-sponse to agents’ prompting and feedback) conditions, and (2) pedagogical agents can adequately and correctly detect, track, model, and foster learners’ self-regulatory processes. In this study, we tested the effectiveness of PAs’ prompting and feedback on learners’ self-regulated learning about the human circulatory system with MetaTutor, an adaptive, multi-agent learning environ-ment. Sixty-nine (N=69) undergraduates learned about the topic with MetaTutor, during a 2-hour session under one of three conditions: prompt and feedback (PF), prompt-only (PO), and no prompt (NP) condition. The PF condition re-ceived timely prompts from several pedagogical agents to deploy various SRL processes and received immediate directive feedback concerning the deployment of the processes. The PO condition received the same timely prompts, without feedback. Finally, the NP condition learned without assistance from the agents. Results indicate that those in the PF condition had significantly higher learning efficiency scores than those in both the PO and control conditions. In addition, log-file data provided evidence of the effectiveness of the PA’s timely scaffolding and feedback in facilitating learners’ (in the PF condition) metacognitive monitoring and regulation during learning.
The experience of academic boredom among students may be universal; in fact, almost all students complain at least occasionally about being bored in class or while studying. Despite the perceived negative influence of boredom on learning, there has been no synthesis of empirical findings underscoring how boredom relates to academic outcomes. Therefore, the purpose of this study is to meta-analyze the research exploring the relationship between students’ academic boredom and their motivation, study strategies and behaviors, and performance. A total of 29 studies, involving 19,052 students, met the inclusion criteria. The overall effect size, = −.24, was significant, p < .001. In subgroup analyses, the negative effect sizes were found to differ between secondary and post-secondary students, and boredom experienced in class had greater negative impact on students’ academic outcomes than boredom experienced while studying. In addition, a significant differential impact of boredom on academic motivation, study strategies and behaviors, and achievement was found. These findings suggest that education professionals should identify strategies to alleviate students’ boredom in academic settings.
The analysis of data collected from the interaction of users with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new research field, learning analytics, and its closely related discipline, educational data mining. This paper first introduces the field of learning analytics and outlines the lessons learned from well-known case studies in the research literature. The paper then identifies the critical topics that require immediate research attention for learning analytics to make a sustainable impact on the research and practice of learning and teaching. The paper concludes by discussing a growing set of issues that if unaddressed, could impede the future maturation of the field. The paper stresses that learning analytics are about learning. As such, the computational aspects of learning analytics must be well integrated within the existing educational research.
The effects of anonymity of the help-seeker and locus of need attribution on help-seeking behavior were investigated. The results indicate that the locus of need attribution affects amount of help-seeking only if the individual remains anonym- ous. When identifiable, the subjects refrained from help-seeking regardless of locus of need attribution. The applied and concep- tual implications of these findings are discussed and the role of anonymity of help-seeker as an important condition in the paradigm of help-seeking behavior is elaborated.
Myriad factors influence students' class participation. This paper develops a path model to delineate students' perceptions of how the classroom's formal and informal structures, along with student attributes, affect self-reported class participation. In our survey, students (n = 1550) report that while organization constrains their participation, students view their interaction with faculty outside the classroom as most important in affecting self-reported participation within it.
Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g., accurate metacognitive judgments followed by the selection of effective learning strategies) and external (e.g., re-sponse to agents’ prompting and feedback) conditions, and (2) pedagogical agents can adequately and correctly detect, track, model, and foster learners’ self-regulatory processes. In this study, we tested the effectiveness of PAs’ prompting and feedback on learners’ self-regulated learning about the human circulatory system with MetaTutor, an adaptive, multi-agent learning environ-ment. Sixty-nine (N=69) undergraduates learned about the topic with MetaTutor, during a 2-hour session under one of three conditions: prompt and feedback (PF), prompt-only (PO), and no prompt (NP) condition. The PF condition re-ceived timely prompts from several pedagogical agents to deploy various SRL processes and received immediate directive feedback concerning the deployment of the processes. The PO condition received the same timely prompts, without feedback. Finally, the NP condition learned without assistance from the agents. Results indicate that those in the PF condition had significantly higher learning efficiency scores than those in both the PO and control conditions. In addition, log-file data provided evidence of the effectiveness of the PA’s timely scaffolding and feedback in facilitating learners’ (in the PF condition) metacognitive monitoring and regulation during learning.
Studies suggest that young children are quite limited in their knowledge about cognitive phenomena—or in their metacognition—and do relatively little monitoring of their own memory, comprehension, and other cognitive enterprises. Metacognitive knowledge is one's stored knowledge or beliefs about oneself and others as cognitive agents, about tasks, about actions or strategies, and about how all these interact to affect the outcomes of any sort of intellectual enterprise. Metacognitive experiences are conscious cognitive or affective experiences that occur during the enterprise and concern any aspect of it—often, how well it is going. Research is needed to describe and explain spontaneous developmental acquisitions in this area and find effective ways of teaching metacognitive knowledge and cognitive monitoring skills. (9 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Examined college students' perceptions of their teachers' support of student questioning (SQ). Perceived teacher support had significant and consistent relationships with students' motivational tendencies and strategy use typical of self-regulated learners. Perceived teacher support affected the likelihood of SQ by influencing whether students had a question to ask and their level of inhibition. Students perceived high levels of support, which does not account for the low incidence of SQ in college classrooms. Agreement between student perceptions and teacher self-reports suggested that creating opportunities for questions and providing high quality answers are important dimensions of teacher support. The possible self-fulfilling consequences of perceived teacher support are discussed. Teacher support for SQ may influence the likelihood that students formulate questions. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The relationship between the grade a student receives and the evaluation the student gives to the instructor has been a topic of concern and extensive investigation in educational research. Ideally, teaching evaluations should be based on teaching effectiveness and not on leniency of grading standards. Previous studies using a variety of methodologies and statistical analyses have found inconclusive results. The present study, after factor analyzing teacher evaluations by 5,878 graduate and undergraduate students into 3 dimensions, found significant relationships between expected grade and the dimensions of Course Effectiveness, Teacher Effectiveness, and Course Difficulty. The 1st set of relationships is viewed as support for a nonbias interpretation of why grades and ratings correlate, whereas the latter 2 sets support a bias interpretation. Correlations between grades and evaluations varied across types of courses, with required courses showing the strongest correlations between grades and ratings. Since these courses are typically less popular than electives, grading leniency may have a greater influence on course evaluations. (15 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This project augments an existing intelligent tutoring system (AutoTutor) that helps learners construct explanations by interacting with them in natural language and helping them use simulation environments. The research aims to develop an agile learning environment that is sensitive to a learner's affective state, presuming that this will promote learning. We integrate state-of-the-art, non- intrusive, affect-sensing technology with AutoTutor in an endeavor to classify emotions on the bases of facial expressions, gross body movements, and conversational cues. This paper sketches our broad theoretical approach, our methods for data collection and evaluation, and our emotion classification techniques.
Metacognition in the form of metacognitive knowledge (MK) -in this case, beliefs about learning-provides a database from which the learner can select strategies for the regulation of learning. However, strategic regulation presupposes that the learner is aware that learning is not progressing well, or fluently, or has failed. This awareness takes the form of metacognitive experiences (ME), that is, feelings, estimates, or judgments related to the features of the learning task, of the cognitive processing as it takes place, or of its outcome. The critical feature of ME is their affective character which gives them access both to the cognitive and the affective regulatory loop of behavior. Being part of the affective loop, ME are related to motivation and self-processes; being part of the cognitive loop, they are connected to MK and to metacognitive skills, the latter being declarative and procedural knowledge. Thus, ME offer awareness that links the present with the past learning experiences and facilitates or inhibits self-regulation of learning in the present as well as in the future.
Modern wearable devices enable the continuous and unobtrusive monitoring of human physiological parameters, including heart rate and electrodermal activity. Through the definition of adequate models these parameters allow to infer the wellbeing, empathy, or engagement of humans in different contexts. In this paper, we show that off-the-shelf wearable devices can be used to unobtrusively monitor the emotional engagement of students during lectures. We propose the use of several novel features to capture students' momentary engagement and use existing methods to characterize the general arousal of students and their physiological synchrony with the teacher. To evaluate our method we collect a data set that -- after data cleaning -- contains data from 24 students, 9 teachers, and 41 lectures. Our results show that non-engaged students can be identified with high reliability. Using a Support Vector Machine, for instance, we achieve a recall of 81% -- which is a 25 percentage points improvement with respect to a Biased Random classifier. Overall, our findings may inform the design of systems that allow students to self-monitor their engagement and act upon the obtained feedback. Teachers could profit of information about non-engaged students too to perform self-reflection and to devise and evaluate methods to (re-)engage students.
Students experience a variety of emotions (or cognitive-affective states) when they are assigned difficult material to learn or problems to solve. We have documented the emotions that occur while college students learn and reason about topics in science and technology. The predominant learning-centered emotions are confusion, frustration, boredom, engagement/flow, curiosity, anxiety, delight, and surprise. A cognitive disequilibrium framework provides a reasonable explanation of why and how these emotions arise during difficult tasks. The student is in the state of cognitive disequilibrium when confronting impasses and obstacles, which launches a trajectory of cognitive-affective processes until equilibrium is restored, disequilibrium is dampened, or the student disengages from the task. Most of our work has been conducted in computerized learning environments (such as AutoTutor and Operation Acquiring Research Investigative and Evaluative Skills! (ARIES)) that help students learn with pedagogical agents that hold conversations in natural language. An emotion-sensitive AutoTutor detects student's emotions and adaptively responds in ways to enhance learning and motivation.
An investigation of student participation in liberal arts and sciences courses at a public university revealed that the amount of participation increased from introductory to upper-division classes, that traditional-age students lagged behind nontraditional students in participation, and that upper-level students participated differently than lower-level students. Student and faculty interviews revealed that each group had different definitions and expectations of student participation.
In an attempt to determine whether male and female students rate teachers differently depending on the gender of the teacher, we analyzed data from 741 classes in which there were at least 10 male and 10 female students. The results revealed small same gender preferences, particularly in female students rating female teachers. Teaching style rather than gender may well explain these preferences.
There is currently little or no support for speakers to learn by reflection when addressing a big audience, like mass lectures, virtual courses or conferences. Reliable feedback from the audience could improve personal skills and work performance. To address this shortcoming we have developed the Live Interest Meter App (LIM App) that supports the gathering, aggregation and visualization of feedback. This application allows audience members to easily provide and quantify their feedback through a simple meter. We conducted several experimental tests to investigate the acceptance and perceived usefulness of the LIM App and a user study in an academic setting to inform its further development. The results of the study illustriate the potential of the LIM App to be used in such scenarios. Main findings show the need for motivating students to use the application, the readiness of presenters to learn retrospectively, and distraction as the main concern of end users.
There is a growing consensus that evidence of teachers' contributions to student learning should be a component of teacher evaluation systems, along with evidence about the quality of teachers' practice. Value-added models (VAMs), designed to evaluate student test score gains from one year to the next are often promoted as tools to accomplish this goal. However, current research suggests that VAM ratings are not sufficiently reliable or valid to support high-stakes, individual-level decisions about teachers. Other tools for teacher evaluation have shown greater success in measuring and improving teaching, especially those that examine teachers' practices in relation to professional standards.
Academic emotions have largely been neglected by educational psychology, with the exception of test anxiety. In 5 qualitative studies, it was found that students experience a rich diversity of emotions in academic settings. Anxiety was reported most often, but overall, positive emotions were described no less frequently than negative emotions. Based on the studies in this article, taxonomies of different academic emotions and a self-report instrument measuring students' enjoyment, hope, pride, relief, anger, anxiety, shame, hopelessness, and boredom (Academic Emotions Questionnaire [AEQ]) were developed. Using the AEQ, assumptions of a cognitive-motivational model of the achievement effects of emotions, and of a control/value theory of their antecedents (Pekrun, 1992b, 2000), were tested in 7 cross-sectional, 3 longitudinal, and 1 diary study using samples of university and school students. Results showed that academic emotions are significantly related to students' motivation, learning strategies, cognitive resources, self-regulation, and academic achievement, as well as to personality and classroom antecedents. The findings indicate that affective research in educational psychology should acknowledge emotional diversity in academic settings by addressing the full range of emotions experienced by students at school and university.
Although student question asking is essential to the learning process, little is known about this communicative phenomenon. More important, the research that is available suggests that students ask far fewer questions than might be expected by educators. This exploratory investigation examined 15 college communication classrooms and found that students asked only an average of 3.3 questions per hour, that male teachers received more questions than female teachers, that female students asked fewer questions than male students in courses taught by males, and that self‐reported masculinity, which includes elements of independence, assertiveness, and a task‐orientation, was associated with a greater likelihood of question asking.
Self-regulated learning (SRL) with hypermedia environments involves a complex cycle of temporally unfolding cognitive and metacognitive processes that impact students’ learning. We present several methodological issues related to treating SRL as an event and strengths and challenges of using online trace methodologies to detect, trace, model, and foster students’ SRL processes. We first describe a scenario illustrating the complex nature of SRL processes during learning with hypermedia. We provide our theoretically driven assumptions regarding the use of several cognitive methodologies, including concurrent think aloud protocols, and provide several examples of empirical evidence regarding the advantages of treating SRL as an event. Last, we discuss challenges for measuring cognitive and metacognitive processes in the context of MetaTutor, an intelligent adaptive hypermedia learning environment. This discussion includes the roles of pedagogical agents in goal-generation, multiple representations, agent-learner dialogue, and a system's ability to detect, track, and model SRL processes during learning.
Mitigating the situational factors that give rise to state boredom is a consistent challenge facing educators. Despite the growing amount of literature devoted to the construct, the field has yet to arrive at a consensus regarding a clear theoretical or operational definition. Subsequently, inconsistencies exist in the assessment methodologies, research findings lack generalizability, and strategies for mitigation in educational settings remain elusive. In this cross-disciplinary analysis, the extant literature on state boredom is critically reviewed and synthesized, and a two-dimensional definition of state boredom as an unpleasant (subjective), low-arousal (objective) experience is proposed. Findings from the technological advances of the last decade that allow for the objective measurement of physiological states are used to inform recommendations for empirically sound assessment methodologies. Finally, the proposed definition of state boredom and related assessment strategies are discussed with respect to implications for enhancing educational practices. Emotions are of critical importance for cognitive development and optimal learning (Linnenbrink-Garcia and Pekrun 2011; Schultz and Pekrun 2007). However, not all emotions are equally relevant to academic achievement. In fact, research has suggested that "basic" emotions (anger, sadness, fear, disgust, happiness, and surprise; Ekman 1992) are rarely experienced during learning sessions (Craig et al. 2008; Lehman et al. 2008a, b). Consequently, researchers have recently begun to distinguish between "basic" and "academic" emotions (Pekrun 2011). Academic emotions refer specifically to those that
Part one of this paper highlights how students today think and process information fundamentally differently from their predecessors, as a result of being surrounded by new technology. The author compares these “digital natives” with the older generation who are learning and adopting new technology naming them “digital immigrants”.
The goal of this study was to integrate previous research conducted on student participation in the college classroom. Numerous studies have been completed on engaging students in classroom discussions, but no study has synthesized this information in the form of an extensive literature review. Here, previous research is pulled together to gain a comprehensive overview of the benefits of participation, logistical issues in participation, student confidence and personality traits in participation, the instructor's influence on and suggestions for increasing participation, the role of sex in participation, and participation in web-based courses. Specifically, academic journal articles that were published over the past 51 years (1958–2009) with student in-class participation as a major variable were included. Details of the selection process, a thorough review of the literature, implications for the classroom, and directions for future research are provided.
The purpose of this investigation was to identify the extent to which college students' self-reports of their in-class participation are related to their impressions of instructors (i.e., credibility, attractiveness, and homophily). Participants were 223 undergraduate students enrolled in an introductory communication course at a large Mid-Atlantic university. Students' self-reports of their in-class participation were positively correlated with perceived instructor social attractiveness, physical attractiveness, background homophily, and attititude homophily, but not with perceived instructor competence, character, caring, and task attractiveness. Furthermore, class size, perceived instructor social attractiveness, and perceived instructor background homophily emerged as significant predictors of in-class participation.
The purpose of this study was to examine the interrelationships among perceived instructor communicator style, perceived instructor trait argumentativeness, and perceived instructor trait verbal aggressiveness in the college classroom. Participants were 236 undergraduate students enrolled in a variety of communication courses at a large Eastern university. Results indicated that (a) perceived instructor trait argumentativeness was positively related to the perceived instructor communicator style attributes of impression leaving, contentious, open, dramatic, dominant, precise, relaxed, attentive, and animated; (b) perceived instructor trait verbal aggressiveness was positively related to the perceived instructor communicator style attributes of contentious and precise, and negatively related to the perceived communicator style attributes of impression leaving, relaxed, friendly, attentive, and animated; and (c) perceived instructor use of verbally aggressive messages was related in some way to the perceived instructor communicator style attributes of contentious, impression leaving, friendly, attentive, animated, relaxed, dramatic, and precise.
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