Explanation in Instructional Communication

Exploration is one of the most common forms of both communication and instruction and yet it is seldom planned or analyzed

AI Techniques in Software Engineering

The idea of using artificial intelligence techniques to support programming has been around for a long time. The earliest notion was to avoid programming entirely. The human user would just tell the computer what to do, without saying how to do it, and the computer would do the right thing. Even if this were feasible, however, it would be much too tedious, since each time the user would have to repeat the details of what he wanted done. So the goal of programming was to explain things to the computer only once, and then later on be able to tell the computer to do the same thing again in some short form, such as the name of the "program." Thus the idea evolved that a user would somehow tell the computer what program was desired, and the computer would write down the program in some internal form so that it could be remembered and repeated later. The assumption was that the resulting program would be correct, complete, efficient, easy to use, and so forth. It would also be exactly what the human user wanted

Supporting peer interaction in online learning environments

This paper reports two studies into the efficacy of sentence openers to foster online peer-to-peer interaction. Sentence openers are pre-defined ways to start an utterance that are implemented in communication facilities as menu’s or buttons. In the first study, typical opening phrases were derived from naturally occurring online dialogues. The resulting set of sentence openers was implemented in a semi-structured chat tool that allowed students to compose messages in a freetext area or via sentence openers. In the second study, this tool was used to explore the students’ appreciation and unprompted use of sentence openers. Results indicate that students hardly used sentence openers and were skeptical of their usefulness. Because both measures were negatively correlated with students’ prior chat experience, optional use of sentence openers may not be the best way to support students’ online interaction. Based on these findings, alternative ways of using sentence openers are discussed and topics for further research are advanced

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of blood cancer prevalent in children and teenagers, characterized by the rapid proliferation of immature white blood cells (WBCs). These atypical cells can overwhelm healthy cells, leading to severe health consequences. Early and accurate detection of ALL is vital for effective treatment and improving survival rates. Traditional diagnostic methods are time-consuming, costly, and prone to errors. The paper proposes an automated detection approach using computer-aided diagnostic (CAD) models, leveraging deep learning techniques to enhance the accuracy and efficiency of leukemia diagnosis. The study utilizes various transfer learning models like ResNet101V2, VGG19, InceptionV3, and InceptionResNetV2 for classifying ALL. The methodology includes using the Local Interpretable Model-Agnostic Explanations (LIME) for ensuring the validity and reliability of the AI system's predictions. This approach is critical for overcoming the "black box" nature of AI, where decisions made by models are often opaque and unaccountable. The paper highlights that the proposed method using the InceptionV3 model achieved an impressive 98.38% accuracy, outperforming other tested models. The results, verified by the LIME algorithm, showcase the potential of this method in accurately identifying ALL, providing a valuable tool for medical practitioners. The research underscores the impact of explainable artificial intelligence (XAI) in medical diagnostics, paving the way for more transparent and trustworthy AI applications in healthcare

Cooperative learning projects as a strategy to foster oral interaction in EFL learners

146 Páginas.La interacción oral tiene un papel importante en el aprendizaje de idiomas. Estudios previos han resaltado que los estudiantes deben ser involucrados en actividades más motivantes, significativas y auténticas como forma de promover y mejorar la interacción oral. Sin embargo, poca atención y valor se le ha dado al aprendizaje cooperativo como estrategia para involucrar a los estudiantes en la interacción oral. El presente estudio de investigación-acción educativa empleó registros de audio, diarios del docente y entrevistas grupales para recolectar datos sobre el impacto que los proyectos cooperativos tuvieron sobre el desarrollo de la interacción oral en los estudiantes. Los datos fueron analizados a la luz de la teoría fundamentada y los resultados demostraron que los estudiantes mejoraron significativamente su interacción oral trabajando cooperativamente en proyectos. Esto justifica la noción de que el aprendizaje cooperativo es un enfoque efectivo para mejorar la interacción oral en niños, por lo tanto, este debería ser ampliamente adoptado por la comunidad educativa

Interpreting natural language processing (NLP) models and lifting their limitations

There have been many advances in the artificial intelligence field due to the emergence of deep learning and big data. In almost all sub-fields, artificial neural networks have reached or exceeded human-level performance. However, most of the models are not interpretable and they perform like a black box. As a result, it is hard to trust their decisions, especially in life and death scenarios. In recent years, there has been a movement toward creating explainable artificial intelligence, but most work to date has concentrated on image processing models, as it is easier for humans to perceive visual patterns. There has been little work in other fields like natural language processing. By making our machine learning models more explainable and interpretable, we can learn about their logic, optimize them by removing bias, overcome their limitations, and make them resistant against adversarial attacks. This research dissertation is concentrated on making deep learning models that handle textual data, more understandable, and also use these insights in order to boost their performance by overcoming some of the common limitations. In addition to that, we use this knowledge to target words for designing efficient and effective textual adversarial attacks

The Nature Of Scientific Explanation (NOSE): Using a philosophically guided framework to examine the nature and quality of scientific explanations constructed by freshman college students, science teachers, and practicing scientists

Issues regarding scientific explanation have been of interest to philosophers from Pre-Socratic times. The notion of scientific explanation is of interest not only to philosophers, but also to science educators as is clearly evident in the emphasis given to K-12 students’ construction of explanations in current national science education reform efforts – the Next Generation Science Standards NGSS (NGSS Lead States, 2013). Nonetheless, there is a dearth of research on conceptualizing explanation in science education. Scientific explanation seems to be ill-defined (or left undefined) among researchers, science teachers and, in turn, students (Braaten & Windschitl, 2010, p. 639). Guided by philosophical models of and approaches to explanation, this study proposed a framework – the Nature of Scientific Explanation (NOSE) – for assessing the type, nature and quality of scientific explanations. Furthermore, to establish the validity and usefulness of the NOSE framework, the study aimed to (a) examine college freshman science students’, secondary science teachers’, and practicing scientists’ explanations, (b) elucidate their perceptions of explanations and how they compare to the formal analytical NOSE framework and (c) characterize the nature of the criteria that participant students, teachers, and scientists deploy when assessing the “validity” of explanations. The following research questions guided the study: (1) How do college freshmen science students’, secondary science teachers’ and practicing scientists’ explanations fare when assessed using the NOSE framework? In other words, what is the nature (structural elements) and quality of participants’ scientific explanations when analyzed using the NOSE framework? (2) How do college freshmen science students’, secondary science teachers’ and practicing scientists’ explanations of scientific phenomena compare and contrast when analyzed using the NOSE framework? (3) What criteria do college freshmen science students, secondary science teachers, and practicing scientists use in judging the quality of scientific explanations? How are these criteria consistent among and/or different across the three groups? (4) To what extent are freshmen science students’, secondary science teachers’ and practicing scientists’ views of the quality of scientific explanations aligned with those of NOSE framework? The study was exploratory in nature. In-depth, semi structured interviews served as the main instrument of data collection. In two separate interviews, participants first constructed explanations of everyday scientific phenomena and then provided feedback on the explanations constructed by other participants. Participants comprised three groups from a large, Midwestern University and neighboring communities: freshman college students, secondary science teachers, and practicing scientists. Each group comprised 10 participants (50% male, 50% female). The study was conducted in two phases. First, during semi-structured individual interviews all participants generated explanations of various scientific phenomena. Interview transcripts were used to generate an explanation map for each participant following procedures of the NOSE framework developed in this study. During the second phase of the study, participants in each group assessed and provided feedback on the explanations generated during the first phase by other participants. The assignment of explanations to be examined was randomized and ensured that each participant assessed all four scenarios. This examination took place in the context of a second, semi-structured interview. All interviews were audiotaped and transcribed verbatim for analysis. Data analysis comprised three phases. The first involved (a) the construction of explanation maps from participant transcripts; (b) analysis of maps and corresponding transcripts for emerging participant criteria; (c) using the NOSE framework to generate a profile of participants’ types and quality of explanations articulated during the first interview; (d) the explanation maps for each group of participants (students, science teachers, and scientists) were examined to generate a full descriptive account or profile of these maps. This analysis resulted in three profiles, one each for the group of participants; and (e) finally the profiles were compared and contrasted to make assertions regarding ways in which students, teachers, and scientists’ explanations were similar or different from NOSE framework analysis. The second phase focused on analyzing transcripts generated during the second interview to characterize participants’ perceptions of the nature of explanations, and derive the criteria deployed by members of the three groups to judge the “validity” or “goodness” of explanations. This resulted in individual profiles as to perceptions of the nature of explanations and criteria used to judge explanations. Profiles within each group of participants were analyzed for general patterns to generate a common set of criteria that each group used in their assessment, when applicable. These common sets were then compared and contrasted across the three groups. The third phase of data analysis focused on comparing and contrasting the sets of criteria derived from the second phase with those NOSE framework. Analysis in this third phase was more conceptual in nature and focused on how the three groups of participants fared in terms of explanation when their explanations were analyzed using NOSE framework. In general, major findings showed that, when analyzed using NOSE framework, participant scientists did significantly “better” than teachers and students. What is more, most participants across all three groups judged as “best” or “complete” or “good” the explanations made by participant scientists, even though group memberships of the explainers were held anonymous. In addition, scientists had more adequate scientific explanations, from a NOSE perspective, in the sense of providing more relevant and accurate structural elements. Analysis showed that participant explanation maps demonstrated similarities and differences across the three groups. Mainly, scientists’ explanations included more pieces of knowledge and lawlike statements, which were relevant and accurate and/or based on prior content knowledge compared to students’ and teachers’ explanations. Participants’ perceptions of explanations differed significantly. Students tended to think of explanation as a “true” answer to a why-question based on observations. However, teachers and scientists tended to perceive explanation as a testable and verifiable tool that provides understanding. More important were the criteria that participants used to assess explanations. Context-dependence and learner-dependence turned out to be two of the most important aspects of explanations considered by participants. In conclusion, the present study highlights the need articulated by many researchers in science education to understand additional aspects specific to scientific explanation. The study highlighted the importance of not only the structural elements that make up a scientific explanation, but also the connectedness of these elements within the context of teaching and learning. The present findings provide an initial framework for judging the validity of students’ and science teachers’ scientific explanations

USTOPIA REQUIREMENTS THOUGHTS ON A USER-FRIENDLY SYSTEM FOR TRANSFORMATION OF PROGRAMS IN ABSTRACTO

Transformational programming is a program development method which is usually applied using 'pen and paper'. Since this requires a lot of clerical work (copying expressions, con- sistent substitution) which is tiresome and prone to error, some form of machine support is desirable. In this paper a number of systems are described that have already been built to this aim. Some of their shortcomings and limitations are identified. Based on experience with program transformation and transformation systems, a long list of features is given that would be useful in an 'utopian' transformation system. This list is presented using an orthogonal division of the problem area. A number of problems with the realisation of some aspects of our 'utopian' system are identified, and some areas for further research are indicated

Naval Considerations in the Russo-Ukrainian War

Russia’s grand military strategy has a distinct maritime bent, the Ukrainian south coast is Russia’s most tangible strategic prize, and naval forces are crucial to holding it. Since the war’s beginning, Ukraine’s carefully planned strategy has applied stress to key elements of Russia’s maritime strategy, and Ukraine’s theory of victory is shaped by maritime considerations as thoroughly as Russia’s

Teacher Educators’ Interactional Identities within Classroom Interaction in the Field of ELT Education

Esta disertación presenta un estudio sobre las identidades interaccionales docente-educador en la educación del idioma inglés. El estudio amplía el Análisis Conversacional con coanálisis y verificación con tres docentes-educador participantes para analizar la representación de sus identidades interaccionales en clases basadas en el lenguaje y en el contenido en tres programas universitarios diferentes en educación del idioma inglés. Los hallazgos discuten la presencia de múltiples identidades interaccionales compuestas de diversas formas de ser y hacer como interactuantes en las organizaciones que ocurren en la interacción en el aula. Estos hallazgos resaltan la relevancia de estudiar las identidades interaccionales docente-educador para explicar cómo ocurre la educación del idioma inglés durante la interacción en el aula.This dissertation presents a study on teacher-educator interactional identities in English language education. The study extends Conversation Analysis by doing co-analysis and member-checking with three participating teacher-educators to analyze the enactment of their interactional identities in language-based and content-based classes in three different undergraduate programs in English language education. Findings discuss the presence of multiple interactional identities composed of varied forms of being and doing as interactants in the occurring organizations of classroom interaction. These findings highlight the relevance of studying teacher-educator interactional identities to explain how English language education occurs during classroom interaction