Validation of Score Meaning for the Next Generation of Assessments

Despite developments in research and practice on using examinee response process data in assessment design, the use of such data in test validation is rare. Validation of Score Meaning in the Next Generation of Assessments Using Response Processes highlights the importance of validity evidence based on response processes and provides guidance to measurement researchers and practitioners in creating and using such evidence as a regular part of the assessment validation process. Response processes refer to approaches and behaviors of examinees when they interpret assessment situations and formulate and generate solutions as revealed through verbalizations, eye movements, response times, or computer clicks. Such response process data can provide information about the extent to which items and tasks engage examinees in the intended ways. With contributions from the top researchers in the field of assessment, this volume includes chapters that focus on methodological issues and on applications across multiple contexts of assessment interpretation and use. In Part I of this book, contributors discuss the framing of validity as an evidence-based argument for the interpretation of the meaning of test scores, the specifics of different methods of response process data collection and analysis, and the use of response process data relative to issues of validation as highlighted in the joint standards on testing. In Part II, chapter authors offer examples that illustrate the use of response process data in assessment validation. These cases are provided specifically to address issues related to the analysis and interpretation of performance on assessments of complex cognition, assessments designed to inform classroom learning and instruction, and assessments intended for students with varying cultural and linguistic backgrounds

Analyse visuelle et cérébrale de l’état cognitif d’un apprenant

Un état cognitif peut se définir comme étant l’ensemble des processus cognitifs inférieurs (par exemple : perception et attention) et supérieurs (par exemple : prise de décision et raisonnement), nécessitant de la part de l’être humain toutes ses capacités mentales en vue d’utiliser des connaissances existantes pour résoudre un problème donné ou bien d’établir de nouvelles connaissances. Dans ce contexte, une attention particulière est portée par les environnements d’apprentissage informatisés sur le suivi et l’analyse des réactions émotionnelles de l’apprenant lors de l’activité d’apprentissage. En effet, les émotions conditionnent l’état mental de l’apprenant qui a un impact direct sur ses capacités cognitives tel que le raisonnement, la prise de décision, la mémorisation, etc. Dans ce contexte, l’objectif est d’améliorer les capacités cognitives de l’apprenant en identifiant et corrigeant les états mentaux défavorables à l’apprentissage en vue d’optimiser les performances des apprenants. Dans cette thèse, nous visons en particulier à examiner le raisonnement en tant que processus cognitif complexe de haut niveau. Notre objectif est double : en premier lieu, nous cherchons à évaluer le processus de raisonnement des étudiants novices en médecine à travers leur comportement visuel et en deuxième lieu, nous cherchons à analyser leur état mental quand ils raisonnent afin de détecter des indicateurs visuels et cérébraux permettant d’améliorer l’expérience d’apprentissage. Plus précisément, notre premier objectif a été d’utiliser les mouvements des yeux de l’apprenant pour évaluer son processus de raisonnement lors d’interactions avec des jeux sérieux éducatifs. Pour ce faire, nous avons analysé deux types de mesures oculaires à savoir : des mesures statiques et des mesures dynamiques. Dans un premier temps, nous avons étudié la possibilité d’identifier automatiquement deux classes d’apprenants à partir des différentes mesures statiques, à travers l’entrainement d’algorithmes d’apprentissage machine. Ensuite, en utilisant les mesures dynamiques avec un algorithme d’alignement de séquences issu de la bio-informatique, nous avons évalué la séquence logique visuelle suivie par l’apprenant en cours de raisonnement pour vérifier s’il est en train de suivre le bon processus de raisonnement ou non. Notre deuxième objectif a été de suivre l’évolution de l’état mental d’engagement d’un apprenant à partir de son activité cérébrale et aussi d’évaluer la relation entre l’engagement et les performances d’apprentissage. Pour cela, une étude a été réalisée où nous avons analysé la distribution de l’indice d’engagement de l’apprenant à travers tout d’abord les différentes phases de résolution du problème donné et deuxièmement, à travers les différentes régions qui composent l’interface de l’environnement. L’activité cérébrale de chaque participant a été mesurée tout au long de l’interaction avec l’environnement. Ensuite, à partir des signaux obtenus, un indice d’engagement a été calculé en se basant sur les trois bandes de fréquences α, β et θ. Enfin, notre troisième objectif a été de proposer une approche multimodale à base de deux senseurs physiologiques pour permettre une analyse conjointe du comportement visuel et cérébral de l’apprenant. Nous avons à cette fin enregistré les mouvements des yeux et l’activité cérébrale de l’apprenant afin d’évaluer son processus de raisonnement durant la résolution de différents exercices cognitifs. Plus précisément, nous visons à déterminer quels sont les indicateurs clés de performances à travers un raisonnement clinique en vue de les utiliser pour améliorer en particulier, les capacités cognitives des apprenants novices et en général, l’expérience d’apprentissage.A cognitive state can be defined as a set of inferior (e.g. perception and attention) and superior (e.g. perception and attention) cognitive processes, requiring the human being to have all of his mental abilities in an effort to use existing knowledge to solve a given problem or to establish new knowledge. In this context, a particular attention is paid by computer-based learning environments to monitor and assess learner’s emotional reactions during a learning activity. In fact, emotions govern the learner’s mental state that has in turn a direct impact on his cognitive abilities such as reasoning, decision-making, memory, etc. In this context, the objective is to improve the cognitive abilities of the learner by identifying and redressing the mental states that are unfavorable to learning in order to optimize the learners’ performances. In this thesis, we aim in particular to examine the reasoning as a high-level cognitive process. Our goal is two-fold: first, we seek to evaluate the reasoning process of novice medical students through their visual behavior and second, we seek to analyze learners’ mental states when reasoning to detect visual and cerebral indicators that can improve learning outcomes. More specifically, our first objective was to use the learner’s eye movements to assess his reasoning process while interacting with educational serious games. For this purpose, we have analyzed two types of ocular metrics namely, static metrics and dynamic metrics. First of all, we have studied the feasibility of using static metrics to automatically identify two groups of learners through the training of machine learning algorithms. Then, we have assessed the logical visual sequence followed by the learner when reasoning using dynamic metrics and a sequence alignment method from bio-informatics to see if he/she performed the correct reasoning process or not. Our second objective was to analyze the evolution of the learner’s engagement mental state from his brain activity and to assess the relationship between engagement and learning performance. An experimental study was conducted where we analyzed the distribution of the learner engagement index through first, the different phases of the problem-solving task and second, through the different regions of the environment interface. The cerebral activity of each participant was recorded during the whole game interaction. Then, from the obtained signals, an engagement index was computed based on the three frequency bands α, β et θ. Finally, our third objective was to propose a multimodal approach based on two physiological sensors to provide a joint analysis of the learner’s visual and cerebral behaviors. To this end, we recorded eye movements and brain activity of the learner to assess his reasoning process during the resolution of different cognitive tasks. More precisely, we aimed to identify key indicators of reasoning performance in order to use them to improve the cognitive abilities of novice learners in particular, and the learning experience in general

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Learning from Teacher's Eye Movement: Expertise, Subject Matter and Video Modeling

How teachers' eye movements can be used to understand and improve education is the central focus of the present paper. Three empirical studies were carried out to understand the nature of teachers' eye movements in natural settings and how they might be used to promote learning. The studies explored 1) the relationship between teacher expertise and eye movement in the course of teaching, 2) how individual differences and the demands of different subjects affect teachers' eye movement during literacy and mathematics instruction, 3) whether including an expert's eye movement and hand information in instructional videos can promote learning. Each study looked at the nature and use of teacher eye movements from a different angle but collectively converge on contributions to answering the question: what can we learn from teachers' eye movements? The paper also contains an independent methodology chapter dedicated to reviewing and comparing methods of representing eye movements in order to determine a suitable statistical procedure for representing the richness of current and similar eye tracking data. Results show that there are considerable differences between expert and novice teachers' eye movement in a real teaching situation, replicating similar patterns revealed by past studies on expertise and gaze behavior in athletics and other fields. This paper also identified the mix of person-specific and subject-specific eye movement patterns that occur when the same teacher teaches different topics to the same children. The final study reports evidence that eye movement can be useful in teaching; by showing increased learning when learners saw an expert model's eye movement in a video modeling example. The implications of these studies regarding teacher education and instruction are discussed.PHDEducation & PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145853/1/yizhenh_1.pd

Investigating Reading Behavior and Inference-making in Advanced L2 Reading Comprehension Assessment Tasks

Despite the ubiquity of reading comprehension tasks in English language proficiency tests (or sections of tests), the constructs underlying successful reading comprehension in English as a second/additional language at the advanced academic level are still not completely understood. Part of the reason for this gap in the current state of knowledge comes from how existing models of second language reading neglect higher-order reading skills. Many reading assessments overly target language proficiency skills and assume the transfer of first language literacy skills, leaving unexamined the higher-order skills of language learners who become skilled academic readers in their second or additional language. This study seeks to address the dearth of research on higher-order reading skills in advanced second language reading comprehension by examining the activation of these skills in realistic L2 reading comprehension tasks. A reading comprehension test with three different tasks (MC questions, cloze, and summary) was developed and administered to 102 second language English and multilingual undergraduate and graduate students studying at a university in the US. Eye-movement behavior was recorded during these tasks, and each reading task was followed by a sentence verification task to measure activation of inferencing. Eye-movement behavior and inferencing are compared across the reading tasks, and additionally compared to language proficiency and reading comprehension scores. The tasks each elicited distinct patterns of reading behavior: the cloze task elicited careful local reading, the MC task elicited expeditious linear reading, and the summary task elicited both careful global reading and expeditious strategies. Cloze scores were closely related to language proficiency, but also related to reasoning ability and processing efficiency. MC scores were unrelated to proficiency. They were instead related more to reasoning ability and were predicted by readers’ ability to efficiently process the MC questions. Inferencing ability was only predictive of score in the summary task. Summary scores were additionally influenced by global attention to the text, processing efficiency, reading motivation, and language proficiency. Implications for the use of each task as L2 reading assessment are discussed, as well as implications for the teaching of second language reading

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Emotional Regulation in Synchronous Online Collaborative Learning: A Facial Expression Recognition Study

Emotional regulation in learning has been recognised as a critical factor for collaborative learning success. However, the “unobservable” processes of emotion and motivation at the core of learning regulation have challenged the methodological progress to examine and support learners’ regulation. Artificial intelligence (AI) and learning analytics have recently brought novel opportunities for investigating the learning processes. This multidisciplinary study proposes a novel fine-grained approach to provide empirical evidence on the application of these advanced technologies in assessing emotional regulation in synchronous computer-support collaborative learning (CSCL). The study involved eighteen university students (N=18) working collaboratively in groups of three. The process mining analysis was adopted to explore the patterns of emotional regulation in synchronous CSCL, while AI facial expression recognition was used for examining learners’ associated emotions and emotional synchrony in regulatory activities. Our findings establish a foundation for further design of human-centred AI-enhanced support for collaborative learning regulation

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Investigating the construct measured by banked gap-fill items:evidence from eye-tracking

This study investigates test-takers’ processing while completing banked gap-fill tasks, designed to test reading proficiency, in order to test theoretically based expectations about the variation in cognitive processes of test-takers across levels of performance. Twenty-eight test-takers’ eye traces on 24 banked gap-fill items (on six tasks) were analysed according to seven on-line eye-tracking measures representing overall, text and task processing. Variation in processing was related to test-takers’ level of performance on the tasks overall. In particular, as hypothesised, lower-scoring students exerted more cognitive effort on local reading and lower-level cognitive processing in contrast to test-takers who attained higher scores. The findings of different cognitive processes associated with variation in scores illuminate the construct measured by banked gap-fill items, and therefore have implications for test design and the validity of score interpretations