Computer Science Concept Inventories: Past and Future

Concept Inventories (CIs) are assessments designed to measure student learning of core concepts. CIs have become well known for their major impact on pedagogical techniques in other sciences, especially physics. Presently, there are no widely used, validated CIs for computer science. However, considerable groundwork has been performed in the form of identifying core concepts, analyzing student misconceptions, and developing CI assessment questions. Although much of the work has been focused on CS1 and a CI has been developed for digital logic, some preliminary work on CIs is underway for other courses. This literature review examines CI work in other STEM disciplines, discusses the preliminary development of CIs in computer science, and outlines related research in computer science education that contributes to CI development

Representaciones de lógica, conjuntos y álgebra booleana a través de un cuestionario

"En este trabajo, partiendo de la problemática existente en referencia al aprendizaje de la asignatura del Álgebra Booleana, que se enseña a nivel superior, se consideró de inicio la articulación conceptual con la lógica y los conjuntos, todo lo cual tiene una incidencia en los aprendizajes de los estudiantes. De tal manera que se presentan los resultados de la investigación, resaltando los procesos de diseño, prueba de validación, confiabilidad y análisis de un cuestionario en formato digital. Dicho cuestionario permitió identificar las dificultades cognitivas que surgen en los estudiantes a raíz de las transformaciones que se llevan a cabo entre representaciones presentes de los ítems y sus respectivas respuestas. Como complemento se consideró anexar un apartado para que los estudiantes enviaran sus evidencias, lo que conllevó a identificar las representaciones semióticas que utilizan para solucionar los reactivos del cuestionario. Cada tema consta de ocho ítems y los temas correspondientes son lógica, conjuntos, álgebra booleana y la articulación entre ellos"

Learning Logic: A Mixed Methods Study to Examine the Effects of Context Ordering on Reasoning About Conditionals

Logical statements are prevalent in mathematics, the sciences, law, and many areas of everyday life. The most common logical statements are conditionals, which have the form “If H..., then C...,” where “H” is a hypothesis (or condition) to be satisfied and “C” is a conclusion to follow. Reasoning about conditionals is a skill that is only superficially understood by most individuals and depends on four main conditional contexts (e.g., intuitive, abstract, symbolic, or counterintuitive). The purpose of this study was to test a theory about the effects of context ordering on reasoning about conditionals. To test the theory, the researcher developed, tested, and revised a virtual manipulative educational mathematics application, called the Learning Logic App. This study employed a convergent parallel mixed methods design to answer an overarching research question and two subquestions. The overarching research question was “How does the order of teaching four conditional contexts influence reasoning about conditionals?” The two subquestions examined this influence on reasoning in terms of performance and perceptions. This study involved two phases. During Phase I, 10 participants interacted with the Learning Logic App in a clinical setting. The researcher used information gathered in Phase I to revise the Learning Logic App for Phase II. During Phase II, 154 participants interacted with the Learning Logic App in a randomly assigned context ordering in an online setting. In both phases, the researcher collected quantitative and qualitative data. After independent analyses, the researcher made meta- inferences from the two data strands. The results of this study suggest that context ordering does influence learners’ reasoning. The most beneficial context ordering for learners’ performance was symbolic-intuitive-abstract-counterintuitive. The most beneficial context ordering for learners’ perceptions was intuitive-abstract-counterintuitive-symbolic. Based on these results, the researcher proposed a new context ordering: symbolic-intuitive-abstract-counterintuitive-symbolic. This progression incorporates a catalyst at the beginning (symbolic context) which aids the learner in reassessing their prior knowledge. Then, the difficulty of the contexts progresses from easiest to hardest (intuitive-abstract-counterintuitive-symbolic). These findings are important because they provide an instructional sequence for teaching and learning to reason about conditionals that is beneficial to both learners’ performance and their perceptions

How visual representations affect undergraduate students’ use and understanding of engineering concepts during problem solving

In this two-part thesis, I address gaps in the surface science of semiconductor materials for photovoltaics and discipline-based education research literature. Surface Science of Semiconductor Materials for Photovoltaics With the rise of global energy consumption, it is important to find low-cost, renewable sources of energy. Photovoltaic devices (i.e., solar cells) are one such source of energy, converting solar energy into electricity. However, their levelized cost of electricity (i.e., $/MW*h) is currently too high to compete with traditional electricity sources. One way to lower this cost is to increase the solar cell’s power conversion efficiency, which is often limited by defects throughout the device. While defect behavior is well-studied in the community, the techniques used are not chemically sensitive, so experiments must be combined with computation to identify defects. Understanding the chemical identity of defects is particularly important for improving the efficiency of solar cells that use CuInSe2 (CIS) and Cu(In,Ga)Se2 (CIGS) as the absorber layer, which are inexpensive but limited due to a variety of defects. Understanding which elements are involved in charge capture and recombination would contribute to the literature by providing a fundamental understanding of defects in materials important to the photovoltaic industry. In this study, I studied charge capture on defects within the CIS side of the CdS/CIS interface using photo-modulated x-ray photoelectron spectroscopy (XPS) to observe changes in surface charging under illumination. My work provides some of the first experimentally verified evidence of electron capture on Cd donor atoms within CIS thin films. Others in the field can use my work to investigate the effect of Ga- or grain boundary-based defects in CIGS thin films or on other materials such as perovskites or CuZnSnS4 whose defect behavior under illumination is also relatively unexplored using chemically-sensitive techniques. Discipline-Based Education Research Whether sketching an idea on the back of a napkin, drawing schematics on a whiteboard, or using computational tools to understand unobservable phenomena, engineers need to be able to solve problems and communicate their knowledge with a variety of visual representations. Research into how students use visual representations has so far focused on questions such as “will a picture or a graph improve students’ problem-solving ability” rather than “what about the graphical representation causes differences in students’ problem-solving ability?” To address this gap, I conducted a sequential exploratory mixed methods study to characterize and test the interplay between students’ level of conceptual knowledge, how concepts are encoded within representations, and how students use concepts during problem solving both within and across two engineering disciplines. The results of my work are 1) three types of features in visual representations that affect student’ problem solving and 2) a classroom intervention based on the findings from the think-aloud interviews to test the generalizability of my first finding. My work contributes to the community by applying a well-known theoretical framework that describes how people process visual information to novel contexts and developing deeper insights into the novice-expert transition. Additionally, my work provides the community with a data-driven theory to analyze how specific types of visual representations might be inhibiting engineering students’ ability to learn concepts and use them effectively when solving problems

Az informatika (programozás) oktatásának módszertani kérdései

AZ INFORMATIKA (PROGRAMOZÁS) OKTATÁSÁNAK MÓDSZERTANI KÉRDÉSEI Összefoglaló A disszertáció az informatika-, ezen belül a programozástudásnak definíciójára építve határozza meg az informatikaoktatás célját, a célhoz illeszkedő oktatási és tanulási módszereket, a képesség- és készségfejlesztési módokat és az informatikai tudás minősítésének szempontjait. A tézisek e rendszer alapjairól és kapcsolatukról szólnak: I. Az informatikatudomány gondolkodási módszereiben és eszközeiben egységes rendszert alkot, amelynek sikeres oktatásához tudományspecifikus oktatás-módszertan szükséges. II. Minden, informatikatantervben előírt témát informatikatudományi megközelítéssel oktatva együtt fejleszthető az informatikai gondolkodás, az alkalmazói készségek és a programozási készségek. III. A LAU alapú leírás, valamint ezek kombinációjaként a LAU-modell egy eszköz a tudás-, a készség- és a képességelemek, illetve a tanulási és tanítási folyamat leírására, jellemzésére. IV. Az informatikai gondolkodás – ezzel együtt a programozás – képességének a fejlesztését és gyakorlását a motiváció, az érzelmek, a mentális állapot katalizátorként segíti vagy blokkolja. A pedagógiai gyakorlatban – a tézisekből következően – az informatika és programozás oktatásának módszere nagymértékben egyénre, tanulóra szabott kell legyen