Instrumenter l’activité des élèves pour orienter la cognition et la métacognition lors des devoirs à domicile

À travers ce mémoire professionnel, nous nous sommes intéressées au fait que certains élèves rencontrent des difficultés lorsqu’ils se retrouvent seuls face à leurs devoirs. Nous nous sommes particulièrement intéressées aux élèves les plus en difficulté, qui ont déjà de la peine à suivre en classe et se retrouvent fréquemment avec un agenda rempli de devoirs de toutes sortes. Ainsi, sans aide externe, ils ne savent pas comment travailler et risquent de tomber dans le « faire » pour compléter plutôt que d’apprendre. Nous avons donc eu pour ambition de créer un outil permettant d’instrumenter l’activité de l’élève pour le soutenir dans son travail. Pour ce faire, nous sommes parties de la grille d’Anderson & Krathwohl, qui énumère six habiletés différentes touchant aux apprentissages. Ce travail s’est porté sur l’habileté « comprendre », qui selon nous, est une habileté essentielle à mobiliser dans de nombreux devoirs. Ensuite, dans le but d’outiller l’élève d’un aidemémoire, nous avons fait tout un travail en amont portant sur l’analyse de l’activité de l’élève d’un point de vue cognitif et métacognitif. Notre attention s’est portée d’une part sur la compréhension de l’élève (cognition), mais également sur sa stratégie de travail lorsqu’il planifie et régule son travail (métacognition). Pour parvenir à nos fins, nous avons mené plusieurs instructions aux sosies : une technique d’entretien qui a pour but d’accéder à l’activité de l’acteur, dans ce cas-ci l’élève. Notre recherche repose sur l’analyse de devoirs et de stratégies d’élèves démontrant certaines difficultés scolaires dans l’écologie d’une classe de 5ème HarmoS (H) et de 6ème HarmoS (H). Au sein de ces deux classes, nous avions la volonté d’apporter un dispositif externe qui permettrait aux élèves, particulièrement ceux en difficulté, de cibler l’attente des devoirs pour mieux les comprendre. Un travail ambitieux qui, par manque de temps, n’a pas pu être mené à terme. Nous avons toutefois été en mesure d’analyser l’activité des élèves et d’y apporter nos interprétations, une étape fondamentale avant d’intégrer un outil médiateur

(A) Cluster analysis of current, former and never smokers: Single link hierarchical clustering using the 609 SAGE tags comprised in Additional file representing tags differentially expressed between current and never smokers

<p><b>Copyright information:</b></p><p>Taken from "Effect of active smoking on the human bronchial epithelium transcriptome"</p><p>http://www.biomedcentral.com/1471-2164/8/297</p><p>BMC Genomics 2007;8():297-297.</p><p>Published online 29 Aug 2007</p><p>PMCID:PMC2001199.</p><p></p> Distance measure used was a Euclidean distance. The visualization package [23] was used for clustering. Green rectangles represent samples with lower expression for the particular gene amongst the samples, and red rectangles represent samples where the gene is highly expressed relative to other samples. (B) Principal component analysis of current, former and never smokers. Expression values used were scaled to tags per million (TPM). Each tag was then normalized by dividing its value by the maximum value for that tag seen in all the libraries. Subsequently, this value was then multiplied by 6 and then subtracted by 3 to put the values ratios in the range of -3 to 3. A co-variance based approach was used and the statistics toolbox in (Mathworks) was used. Current smokers are represented in red, former smokers are represented in blue and never smokers are represented in green

(A) SAGE library statistics: Summary statistics of the 24 SAGE libraries analyzed in this study

<p><b>Copyright information:</b></p><p>Taken from "Effect of active smoking on the human bronchial epithelium transcriptome"</p><p>http://www.biomedcentral.com/1471-2164/8/297</p><p>BMC Genomics 2007;8():297-297.</p><p>Published online 29 Aug 2007</p><p>PMCID:PMC2001199.</p><p></p> Mapping information was based on the May 10th, 2006 version of [45]. In total, over 3,000,000 SAGE tags were sequenced, with over 110,000 unique tags represented upon the exclusion of super singleton tags. (Super singleton tags are tags which have a count of 1 in a single library only). Approximately 75 % of these 110,000 unique tags, (potentially representing as many unique transcripts), mapped to an annotated cluster. As multiple SAGE tags frequently map to the same cluster, we have identified at a total of 25,653 distinct clusters within our dataset, approximately 68% of which represent previously characterized genes. Notably, 25% of the unique tags had no mapping, suggesting much information is currently unknown. (B) Transcriptome Venn diagram: Venn diagram of the transcriptomes of current, former and never smokers. Reported is the number of tags which are expressed in every library group at a raw tag count greater than or equal to 2, representing the tags which are constitutively expressed in each set. Nearly 2000 SAGE tags, mapping to over 1700 genes are common to all 24 SAGE libraries. A lower number of never smokers may have contributed to a higher number of preferentially expressed transcripts in this group

SAGE and quantitative PCR (qRT-PCR) analysis of select genes: (A) Genes found to have reversible expression upon smoking cessation

<p><b>Copyright information:</b></p><p>Taken from "Effect of active smoking on the human bronchial epithelium transcriptome"</p><p>http://www.biomedcentral.com/1471-2164/8/297</p><p>BMC Genomics 2007;8():297-297.</p><p>Published online 29 Aug 2007</p><p>PMCID:PMC2001199.</p><p></p> Box plots of SAGE data and histograms for qRT-PCR for and . Distribution of ratios between both current vs. former and current vs. former and never (Additional file IV) were found to be statistically different. (B) Genes found to be either partially or fully irreversible. Box plots of SAGE data and histograms for qRT-PCR for and . Distribution of ratios between current vs. former and former vs. never were statistically different for and in addition, was statistically significant for the combination of current and former vs. never. Box plot analysis was done using the Statistics toolbox from the program. Red lines in the boxes represent the median expression value in terms of tags per million (TPM), and red "plus" signs represent outliers (values which are greater than 1.5 times the maximum value). The bottom and top part of the boxes represent the 2and 3quartiles of the data respectively. The error bars represent the 5and 95percentiles of the data. Quantitative RT-PCR validation was performed on a second cohort of nine current smokers, seven former smokers and six never smokers. Plotted is the average expression ratio relative to the average expression in never smokers of current (red), former (blue) and never (green) smokers. Statistical significance was determined using a one-tailed p-value from the Mann Whitney U Test (Supplemental Table IX)

MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data-0

S of interest (i.e. amplifications, deletions), save them to the database, and create ISCN reports. Annotating regions can be used side by side with segmentation probabilities to verify the called regions and can also be used to compare amplification and deletions across multiple samples or create Frequency Plots. Numbers indicate genomic view of (1) annotated regions and (2) segmentation calls, and chromosome view of (3) annotation form where user can mark the region as an amplification, gain, deletion, loss or neutral region and (4) segmentation calls which aid in making the calls.<p><b>Copyright information:</b></p><p>Taken from "MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data"</p><p>http://www.biomedcentral.com/1471-2105/9/243</p><p>BMC Bioinformatics 2008;9():243-243.</p><p>Published online 20 May 2008</p><p>PMCID:PMC2408605.</p><p></p

Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines-3

<p><b>Copyright information:</b></p><p>Taken from "Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines"</p><p>http://www.molecular-cancer.com/content/7/1/2</p><p>Molecular Cancer 2008;7():2-2.</p><p>Published online 7 Jan 2008</p><p>PMCID:PMC2254646.</p><p></p>reen labeled FISH probes, respectively

Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines-0

<p><b>Copyright information:</b></p><p>Taken from "Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines"</p><p>http://www.molecular-cancer.com/content/7/1/2</p><p>Molecular Cancer 2008;7():2-2.</p><p>Published online 7 Jan 2008</p><p>PMCID:PMC2254646.</p><p></p

Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines-1

<p><b>Copyright information:</b></p><p>Taken from "Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines"</p><p>http://www.molecular-cancer.com/content/7/1/2</p><p>Molecular Cancer 2008;7():2-2.</p><p>Published online 7 Jan 2008</p><p>PMCID:PMC2254646.</p><p></p

Pathways of the differentially expressed genes in HL-derived and ALCL-derived cell lines

<p><b>Copyright information:</b></p><p>Taken from "Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines"</p><p>http://www.molecular-cancer.com/content/7/1/2</p><p>Molecular Cancer 2008;7():2-2.</p><p>Published online 7 Jan 2008</p><p>PMCID:PMC2254646.</p><p></p

Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines-2

<p><b>Copyright information:</b></p><p>Taken from "Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines"</p><p>http://www.molecular-cancer.com/content/7/1/2</p><p>Molecular Cancer 2008;7():2-2.</p><p>Published online 7 Jan 2008</p><p>PMCID:PMC2254646.</p><p></p