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

What Was I Thinking? Eye-Tracking Experiments Underscore the Bias that Architecture Exerts on Nuclear Grading in Prostate Cancer

We previously reported that nuclear grade assignment of prostate carcinomas is subject to a cognitive bias induced by the tumor architecture. Here, we asked whether this bias is mediated by the non-conscious selection of nuclei that “match the expectation” induced by the inadvertent glance at the tumor architecture. 20 pathologists were asked to grade nuclei in high power fields of 20 prostate carcinomas displayed on a computer screen. Unknown to the pathologists, each carcinoma was shown twice, once before a background of a low grade, tubule-rich carcinoma and once before the background of a high grade, solid carcinoma. Eye tracking allowed to identify which nuclei the pathologists fixated during the 8 second projection period. For all 20 pathologists, nuclear grade assignment was significantly biased by tumor architecture. Pathologists tended to fixate on bigger, darker, and more irregular nuclei when those were projected before kigh grade, solid carcinomas than before low grade, tubule-rich carcinomas (and vice versa). However, the morphometric differences of the selected nuclei accounted for only 11% of the architecture-induced bias, suggesting that it can only to a small part be explained by the unconscious fixation on nuclei that “match the expectation”. In conclusion, selection of « matching nuclei » represents an unconscious effort to vindicate the gravitation of nuclear grades towards the tumor architecture

Fixation duration and the learning process: an eye tracking study with subtitled videos

Learning is a complex phenomenon and education researchers are increasingly focussing on processes that go into it. Eye tracking has become an important tool in such research. In this paper, we focus on one of the most commonly used metrics in eye tracking, namely, fixation duration. Fixation duration has been used to study cognition and attention. However, fixation duration distributions are characteristically non-normal and heavily skewed to the right. Therefore, the use of a single average value, such as the mean fixation duration, to predict cognition and/or attention could be problematic. This is especially true in studies of complex constructs, such as learning, which are governed by both cognitive and affective processes. We collected eye tracking data from 51 students watching a 12 min long educational video with and without subtitles. The learning gain after watching the video was calculated with pre- and post-test scores. Several multiple linear regression models revealed a) fixation duration can explain a substantial fraction of variation in the pre-post data, which indicates its usefulness in the study of learning processes; b) the arithmetic mean of fixation durations, which is the most commonly reported eye tracking metric, may not be the optimal choice; and c) a phenomenological model of fixation durations where the number of fixations over different temporal ranges are used as inputs seemed to perform the best. The results and their implications for learning process research are discussed

Studies in Analytical Chemistry and Chemical Education. Part 1: Characterization of Complex Organics By Raman Spectroscopy and Gas Chromatography. Part 2: Differential Item Functioning on Multiple-choice General Chemistry Assessments

PART 1: CHARACTERIZATION OF COMPLEX ORGANICS BY RAMAN SPECTROSCOPY AND GAS CHROMATOGRAPHY. The analytical chemistry component of this thesis focused on instrumentation and methods to address challenges in art conservation, particularly the identification, quantitation, and reactivity of a set of representative varnishes and their degradation products. Methods for characterizing varnishes are of great interest to art conservators to restore art work more accurately. A database was created as a means to identify and quantify the composition of aged varnishes. Fourier Transform (FT)-Raman Spectroscopy was used to study common organic acids found in varnishes. The database included nine short-chain carboxylic acids, four di-carboxylic acids, and six medium-to-long-chain fatty acids. Four varnish samples (Linseed Oil, Tung Oil, Dammar, and Mastic) were studied as well. Through visual comparison and fingerprinting analysis comparison, identification of components in the Raman Spectral Database were recognized as components of the varnish samples. Singular Value Decomposition (SVD) was conducted to determine how well the database represented the unknown varnish samples. SVD was applied to the 19 standards collected in building the database. To reduce the amount of data, seven singular values were chosen. The seven singular values were then used to model several unknowns - Linseed Oil, Tung Oil, Dammar, and Mastic. The root-mean square (RMS) error for the unknowns were 0.08, 0.13, 0.21, and 0.21 Raman Intensity units, for Linseed Oil, Tung Oil, Dammar, and Mastic, respectively. If those values are compared to the largest peak in the unknown spectra, the % relative RMS errors are 1.7%, 1.7%, 4.9%, and 6.4%, respectively. A method based upon Gas Chromatography (GC) was developed to characterize carboxylic acids formed as a result of varnish degradation. In this method, a headspace solid-phase microextraction (SPME) approach was optimized in which a 75 µm carboxen-polydimethylsiloxane (CAR/PDMS) SPME fiber was used to analyze mono carboxylic acids. For quantitative determinations, the injection port was in the splitless mode and held at 250°C for 1.0 min for the desorption of the analytes from the SPME fiber. After the initial minute, the injector was switched to a 1:100 split ratio. The temperature program consisted of the oven being initially set to a temperature of 30°C and held for 1 min, and then ramped at 25°C/min to 200°C, where the temperature was held for 1 min, thereby resulting in a total run time of 8.80 min. The PFPD was held at 200 °C for the entire run with a 0.5 ms gate delay, and the gate width was set to 20.0 ms. The mono carboxylic acids that were studied were Formic, Acetic, Propanoic, Butyric, Valeric, and Caproic Acid. A linear relationship was observed between the number of carbons in the carboxylic acid and the retention time (y = 0.75x + 1.55, R2=0.95). Quantitation of Acetic Acid was done by calibration using a first-order regression fit. The model yielded: y = 0.29x + 0.92 (R2=0.95). Using a second-order model, a better fit was found: y = 0.0025x2 - 0.0016x + 5.9 (R2=0.99). An ageing chamber was designed, fabricated, and tested as a means for better understanding the decomposition of varnishes over time as a function of temperature, humidity, and ultraviolet light. The goal in the development of the ageing chamber was to demonstrate that it may be possible to create Standard Reference Materials (SRMs) artificially that resemble authentically aged varnishes. This is possible by the use of the ageing chamber that was built because it is directly incorporated into a GC oven where temperature, where UV radiation, humidity levels, and pollutants can be precisely controlled and carefully monitored. The GC method for carboxylic acids described above was developed to aid in the measurement of carboxylic acid fragments that could arise from the ageing process. There are promising results of the Raman Intensity increasing as the sample aged. PART 2: DIFFERENTIAL ITEM FUNCTIONING ON MULTIPLE-CHOICE GENERAL CHEMISTRY ASSESSMENTS. Over the past 30 years, there have been a plethora of studies on gender differences. Some of the earlier studies found that male students typically outperform female students in visual-spatial and quantitative abilities, whereas female students outperform male students in verbal abilities. In later studies it was reinforced that female students still tended to outperform male students in verbal abilities while the gap in science and mathematics (the latter as an extension of visual-spatial and quantitative abilities) closed greatly. During this same time, more female students entered the science, technology, engineering, and mathematics (STEM) fields. In 1966, only 25% of all STEM bachelor\u27s degrees were obtained by female students, whereas in 2010 that percentage had grown to 50%. Specifically in chemistry, 49.9% of the bachelor\u27s degrees were earned by women compared to the 18.5% in 1966.1 With assessments as a large source of the student\u27s overall course grade, it is imperative that those assessments be valid and unbiased. One way to determine this is to use Differential Item Functioning (DIF). DIF occurs when subgroups of equal abilities perform statistically different on an item on an assessment where typically students that are matched with equivalent ability would have an equivalent possibility of answering the question on the assessment correctly. Because of the difficulty in determining students\u27 ability often times the subgroups are matched on their proficiency or the score they received on an assessment. This dissertation focused on four main questions. The first question focused on identifying items that exhibited DIF. The second question was to determine if DIF was real, i.e. did it persist no matter the set of students or the matching criteria used? The third question focused on determining the causes of DIF by cloning the items by content and construct (format). Lastly, it was hypothesized that one of the reasons behind why DIF is happening was due to the students\u27 problem-solving process and examining these through the use of incorrect heuristics. Data for the first part of the study was collected from two American Chemical Society‐Examinations Institute (ACS‐EI) trial tests (Form A and Form B) that were given to students who had completed one term of general chemistry. This data was analyzed using the Mantel‐Haenszel statistic to determine which items exhibited possible DIF. Along with the Mantel‐Haenzel statistic a two stage DIF analysis2 was conducted. Out of the 140 items, 33 exhibited DIF. On Form A there were 14 items which exhibited DIF, seven that favored male students and seven that favored female students. On Form B there were 19 items which exhibited DIF, 11 that favored female students and eight that favored male students. Those items that exhibited the highest probability of DIF were cloned and included on hourly examinations. These items were examined for DIF persistence against both stages of the two-stage analysis and other relevant measures of proficiency. As more results were collected, patterns emerged for persistent DIF items. On the 24 hourly examinations that were included in this analysis, there were a total of 687 items: 33 (5%) had a significant value using the Mantel-Haenszel statistic, thereby exhibiting persistent DIF. Of those 33 items, 15 were flagged with persistent DIF that favored female students and 18 were flagged with persistent DIF that favored male students. On the three standardized examinations, there were a total of 140 items; 19 (14%) had a significant value using the Mantel-Haenszel statistic, thereby exhibiting persistent DIF. Of those 19 items, two of the items that were flagged with persistent DIF favored female students and 17 of the items that were flagged with persistent DIF favored male students. Along with these items, certain content areas and formats of the items were found to favor one gender. Over six semesters of testing, the content areas that consistently showed DIF that favored male students were measurement (density), greatest/least number of atoms, limiting reagents, ideal gas equation, and crystal structures; the content areas that favored female students were nomenclature and molecular orbital theory. The formats that tended to favor male students were visual-spatial, reasoning, and computation; the format that favored female students was specific chemical knowledge. By cloning these items, it was found that some of the possible causes of persistent DIF for certain items were the content and/or the format. Lastly semi-structured interviews were conducted and it was found that for seven items the possible reason why DIF was happening was due to one subgroup using an incorrect heuristic. These items were in the specific content areas of measurement (density), greatest/least number of atoms, stoichiometry-general, and crystal structures. Additionally, the format inclusions of visual-spatial, reasoning, and computation for these items could also be contributing factors to the observed results. References 1. S&E Degrees: 1966-2010: National Center for Science and Engineering Statistics. http://www.nsf.gov/statistics/nsf11316/content.cfm?pub_id=4062&id=2 (accessed May 26). 2. Zenisky, A. L.; Hambleton, R. K., Detection of Differential Item Functioning in Large-Scale State Assessments: A Study Evaluating a Two-Stage Approach. Educational and Psychological Measurement 2003a, 63 (1), 51-64