Poor informational reading and writing skills in early grades and the need to provide students more experience with informational text have been identified by research as areas of concern. Wilkinson and Son (2011) support future research in dialogic approaches to investigate the impact dialogic teaching has on comprehension. This study (N = 39) examined the gains in reading comprehension, science achievement, and metacognitive functioning of individual second grade students interacting with instructors using dialogue journals alongside their textbook. The 38 week study consisted of two instructional phases, and three assessment points. After a period of oral metacognitive strategies, one class formed the treatment group (n=17), consisting of two teachers following the co-teaching method, and two classes formed the comparison group (n=22). The dialogue journal intervention for the treatment group embraced the transactional theory of instruction through the use of dialogic interaction between teachers and students. Students took notes on the assigned lesson after an oral discussion. Teachers responded to students’ entries with scaffolding using reading strategies (prior knowledge, skim, slow down, mental integration, and diagrams) modeled after Schraw’s (1998) strategy evaluation matrix, to enhance students’ comprehension. The comparison group utilized text-based, teacher-led whole group discussion. Data were collected using different measures: (a) Florida Assessments for Instruction in Reading (FAIR) Broad Diagnostic Inventory; (b) Scott Foresman end of chapter tests; (c) Metacomprehension Strategy Index (Schmitt, 1990); and (d) researcher-made metacognitive scaffolding rubric. Statistical analyses were performed using paired sample t-tests, regression analysis of covariance, and two way analysis of covariance. Findings from the study revealed that experimental participants performed significantly better on the linear combination of reading comprehension, science achievement, and metacognitive function, than their comparison group counterparts while controlling for pretest scores. Overall, results from the study established that teacher scaffolding using metacognitive strategies can potentially develop students’ reading comprehension, science achievement, and metacognitive awareness. This suggests that early childhood students gain from the integration of reading and writing when using authentic materials (science textbooks) in science classrooms. A replication of this study with more students across more schools, and different grade levels would improve the generalizability of these results.

I den här uppsatsen görs det en undersökning vars syfte är att undersöka hur två digitala läromedel (Digilär svenska 1 för gymnasiet och Metafor 1) framställer momentet läsning och läsning av digital text. Undersökningen drivs av tre frågeställningar vilka behandlar läromedlens stoff kring digital text, uppgifternas kunskapsperspektiv och utnyttjande av det digitala mediets potential. Metodvalet bygger på Selander samt Åkerfeldt och Selander (2016; 2011) vilket syftar till att undersöka och värdera läromedel. Teorin bygger på Granaths och Estling Vanneståls (2008) olika synsätt på användandet av IT i undervisningen. Undersökningen påvisar sammanfattningsvis att Digilärs svenska 1 för gymnasiet inte har någon tydlig inriktning mot digital text även om digital text förekommer i det undersökta avsnittet. Detta är problematiskt då forskningen mycket tydligt pekar på att digitala texter är mer svårlästa än papperstexter sett till läsförståelse. Forskningen pekar också på att dessa problem bör gå att avhjälpa med lämpliga lässtrategier. Visst återfinns det en lässtrategi i Digilär, det väldiga frågebatteriet till litteraturanalysen, vilken skulle kunna tänkas sänka läshastigheten och därigenom borga för ökad läsförståelse. Dock paketeras detta inte som en lässtrategi utan som en mall för litteraturanalys. Trots att läromedlet inte tar höjd för läsförståelse i någon primär utsträckning bör det inte likställas med att elever är helt utan hopp om att lära sig. Lärarna finns kvar. Lärarna kan lära ut. Läromedel från ett förlag eller dylikt är ingen absolut nödvändighet för att lärande ska ske. Ett stort ansvar och stor makt finns fortfarande hos lärarna. Är inte ett läromedel nog bra för att i någon större mån utveckla läsförståelsen hos eleverna finns alltså läraren kvar som en backup. Läraren kan även anpassa läromedlet till belåtenhet. Metafor 1, å sin sida, har ett arbetsområde där kopplingen till digital text och för den delen läsförståelse är mycket tydlig. I uppgifterna vilka är riktade mot läsning av digital text finns det inslag av metakognitiv karaktär, av sociokulturell karaktär och av konstruktivistisk karaktär. Kanske ännu viktigare: det finns lässtrategier representerade. Beprövade strategier vilka syftar till att hjälpa eleverna att öka sin läsförståelse när dessa läser digitala texter. Beprövade strategier vilka möter forskningen som är gjord på området, exempelvis att sänka läshastigheten vid läsning av digital text. / The aim of this study is to investigate how two digital learning materials (Digilär svenska 1 för gymnasiet and Metafor 1) presents reading and reading of digital texts. The study is propelled by three questions which circles around the learning materials content, the design and perspectives of the assignments and how well the potential of the digital media is being utilized. The study has shown, in summary, that Digilär does not have an explicit focus on digital texts even though digital texts, by nature, appears in the learning material. This is problematic due to the fact that research shows that digital texts are harder to read than analogue texts from the perspective of reading comprehension. Research also shows that the difficulty there is in reading digital texts can be solved by the use of reading strategies. Metafor 1, on the other hand, has a special subject dedicated to digital texts and the reading of digital texts. The assignments, which are aimed towards reading of digital texts, does have reading strategies represented. The conclusion is therefore that Metafor 1 meets the demands of the previous research in a much for explicit and clear-cut way than Digilär. Digilär does not bring the subject of reading digital texts to light even closely as explicit as Metafor 1.

No description available.

This paper describes the functions performed by a program written to support syntax-directed editing of Emily algebraic specifications in the GNU EMACS text editor. The Emily algebraic specification language is a strongly-typed functional language which can be used to define abstract data types. The Emily language uses term rewriting rules to perform computations and is based on the algebraic specification language in [ANT089].</p> <p>The program uses the binary choice method and recursive reduction, which guarantee that the resultant specifications satisfy the properties of completeness, parsimony, and termination ([ANT089]). The program is written in EMACS LISP under the UNIX operating system, with subprocesses written in C. An Emily translator, written in C, is called to parse the specifications and check for certain types of errors [SHAW89]. / Master of Science

Scholars have considered Italian chamber recitative laments only a transitional phenomenon between madrigal laments and laments organized on the descending tetrachord bass. However, the recitative lament is distinguished from them by its characteristic attitude toward the relationship between music and text. Composer of Italian chamber recitative laments attempted to express more subtle, refined and sometimes complicated emotion in their music. For that purpose, they intentionally created discrepancies between text and music. Sometimes they even destroy the original structure of text in order to clearly deliver the composer's own voice. The basic syntactic structure is deconstructed and reconstructed along with their reading and according to their intention. The discrepancy between text and music is, however, expectable and natural phenomena since text cannot be completely translated or transformed to music and vice versa. The composers of Italian chamber recitative laments utilized their innate heterogeneity between two materials (music and text) as a metaphor that represents the semantic essence of the genre, the conflict. In this context, Italian chamber recitative laments were a real embodiment of the so-called seconda prattica and through the study of them, finally, we more fully able to understand how the spirit of late Renaissance flourished in Italy in the first four decade of the seventeenth century.

Diseases take a central role in biomedical research; many studies aim to enable access to disease information, by designing named entity recognition models to make use of the available information. Disease recognition is a problem that has been tackled by various approaches of which the most famous are the lexical and supervised approaches. However, the aforementioned approaches have many drawbacks as their performance is affected by the amount of human-annotated data set available. Moreover, lexicalapproachescannotdistinguishbetweenrealmentionsofdiseasesand mentionsofotherentitiesthatsharethesamenameoracronym. Thechallengeofthis project is to find a model that can combine the strengths of the lexical approaches and supervised approaches, to design a named entity recognizer. We demonstrate that our model can accurately identify disease name mentions in text, by using word embedding to capture context information of each mention, which enables the model todistinguishifitisarealdiseasementionornot. Weevaluateourmodelusingagold standard data set which showed high precision of 84% and accuracy of 96%. Finally, we compare the performance of our model to different statistical name entity recognition models, and the results show that our model outperforms the unsupervised lexical approaches.

The knowledge of a protein’s function is essential to many studies in molecular biology, genetic experiments and protein-protein interactions. The Gene Ontology (GO) captures gene products' functions in classes and establishes relationship between them. Manually annotating proteins with GO functions from the bio-medical litera- ture is a tedious process which calls for automation. We develop a novel, dictionary- based method to annotate proteins with functions from text. We extract text-based features from words matched against a dictionary of GO. Since classes are included upon any word match with their class description, the number of negative samples outnumbers the positive ones. To mitigate this imbalance, we apply strict rules before weakly labeling the dataset according to the curated annotations. Furthermore, we discard samples of low statistical evidence and train a logistic regression classifier. The results of a 5-fold cross-validation show a high precision of 91% and 96% accu- racy in the best performing fold. The worst fold showed a precision of 80% and an accuracy of 95%. We conclude by explaining how this method can be used for similar annotation problems.

Although the implementation of the Common Core State Standards has included more informational texts in early grades to emphasize reading to gain knowledge (Green, 2012; Roskos & Neuman, 2014), the lack of available expository assessments leaves teachers unsure of what students need to know in order to be successful comprehenders of these texts (Hall, Markham, & Culatta, 2005; Harding, 2014). Moreover, there are very few early expository assessments available which makes it difficult for teachers to monitor young children's expository text knowledge and skills and then adjust their instruction to meet children's unique needs. The EECA R-2 is an early expository assessment measure that was created in order to meet these demands. Data from 128 preschoolers between the ages of four and five in seven different Title I classrooms were collected to determine the reliability and validity of the EECA R-2. Children were given the Test of Story Comprehension (TSC) subtest of the Narrative Language Measures (NLM) Preschool Assessment as well as two versions of the EECA R-2. A Many Facets Rasch model was used to determine reliability and to allow for examination of individual test items. In addition, correlations were run between the NLM and the EECA to determine the validity of the EECA. Results indicate the EECA R-2 is a reliable and valid measure. High reliability was obtained for all facets (rater .00, person .97, and form .96). Items 6-13, 15, 18, and 23 were deemed quality items. Items 1, 2, 5, 15, 16, 20, 21, 25, and 26 were deemed problematic items. There was a positive correlation between the TSC subtest of the NLM Preschool Assessment and the EECA R-2 tasks (r=.76, p= ≤.01). Future research could include another rendition of the EECA in order to gain a better understanding of the problematic items. Doing so could support teachers in assessing student abilities and preparing classroom instruction that targets specific areas of focus to move learning to higher levels.

No description available.

Developing intelligent systems that can recognize and express human affects is essential to bridge the gap between human and artificial intelligence. This thesis explores the creative and emotional frontiers of artificial intelligence. Specifically, in this thesis, we investigate the relation between the affective impact of visual stimuli and natural language by collecting and analyzing a new dataset called ArtEmis. Furthermore, capitalizing on this dataset, we demonstrate affective AI models that can emotionally talk about artwork and generate them given their affective descriptions. In text-to-image generation task, we present HyperCGAN: a conceptually simple and general approach for text-to-image synthesis that uses hypernetworks to condition a GAN model on text. In our setting, the generator and the discriminator weights are controlled by their corresponding hypernetworks, which modulate weight parameters based on the provided text query. We explore different mechanisms to modulate the layers depending on the underlying architecture of a target network and the structure of the conditioning variable.