Didactic Engineering as a research method in physics teaching

Several mathematical concepts have application in physics; this paper reports the results of a pedagogical investigation based on the methodology known as didactic engineering, which has been proposed with the aim of improving the skills exhibited by a group of eleventh grade students from a private institution in the interpretation of kinematic graphs. The concept of the slope of the straight line and how this mathematical concept can represent velocity or acceleration according to the variables considered in the kinematics graph provided was enhanced in the pedagogical intervention. This methodology offers a process of internal validation, after comparing the results obtained in a knowledge test at two different points in time (pre-test and post-test). The results allowed us to identify improvements in all students in terms of basic kinematics graph interpretation skills, and a group of students stood out by advancing in more complex reasoning and interpretation processes. These results contribute to the improvement of the pedagogical practices of Physics teachers at secondary and middle school levels, making them more competent when they enter higher education.

A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting

This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It relies on through-the-sensor (TTS) assessment, by which the sensors’ performances can be measured in situ through processing of their recorded data, yielding the local mine recognition probability, and false alarm rate. The method constructs a risk map of the minefield area composed of small grid cells (~4 m2) that are colour coded according to the remaining mine probability. The new approach can produce this map using the available evidence whenever decision support is needed during the mine hunting operation, e.g., for replanning purposes. What distinguishes the new technique from other recent TTS methods is its use of Bayesian networks that facilitate more complex reasoning within each grid cell. These networks thus allow for the incorporation of two types of evidence not previously considered in evaluation: the explosions that typically result from mine neutralization and verification of mine destruction by visual/sonar inspection. A simulation study illustrates how these additional pieces of evidence lead to the improved estimation of the number of deployed mines (M), compared to results from two recent TTS evaluation approaches that do not use them. Estimation performance was assessed using the mean squared error (MSE) in estimates of M.

Comparison of Attention-based Deep LearningModels for EEG Classification

Deep Learning (DL) has recently shown promising classification performance in Electroencephalography (EEG) in many different scenarios. However, the complex reasoning of such models often prevent the user to explain their classification abilities. Attention, one of the most recent and influential ideas in DL, allows the models to learn which portions of the data are relevant to the final classification output. In this work, we compared three attention-enhanced DL models, the brand-new InstaGATs , an LSTM with attention and a CNN with attention. We used these models to classify normal and abnormal, including artifactual and pathological, EEG patterns in three different datasets. We achieved the state of the art in all classification problems, regardless the large variability of the datasets and the simple architecture of the attention-enhanced models. Additionally, we proved that, depending on how the attention mechanism is applied and where the attention layer is located in the model, we can alternatively leverage the information contained in the time, frequency or space domain of the EEG dataset. Therefore, attention represents a promising strategy to evaluate the quality of the EEG information, and its relevance for classification, in different real-world scenarios.

Mindset Patterns of Newcomers to Organic Farming in Hungary

Counterurbanization, rural in-migrant trend studies rarely focus on the individual decision-making process. This paper studies the mindset patterns and frames the decision to select organic farming as a next career. We aimed to deepen our understanding of the complex reasoning that motivates newcomers to choose organic farming on a personal level. Based on semi-structured interviews, we developed a questionnaire and collected data from the newcomer to organic farming community in Hungary. The responses were analyzed using: (1) factor analysis to assess the dimensionality of the factors and (2) knowledge-based expert system to identify the logical connections between the aspirations. Our conceptual model was developed based on if-then rules between the identified aspirations, which describe the mindset patterns of newcomers to organic farming.

Not on Bread Alone Doth Man Liveth (Deut. 8:3; Mat 4:4): Some Iconoclastic Views on Populism, Democracy, the Rule of Law and the Polish Circumstance

Polarization in today’s politics, pre- and post COVID, transcends nations, states regions and continents. It’s a feature of politics which, in and on itself, when played to extremes by demonizing one’s opponents, it threatens democracy itself—since it frays the demos some cohesion of which is necessary for the legitimacy of majoritarianism, one of the pillars of national democracies. Its lexical manifestation is to be found with expressions such as ‘traitors’ or ‘not real’ Americans, Italians, Israelis—take your pick and fill in the gap.It has, lamentably in my view, a spillover effect also into the academic world of scholarship. A word of criticism of, say, the European Court of Justice instantly brands you a ‘Eurosceptic’ and one of ‘them’. To speak of Universal Values, casts you as an enemy of this or that national cause. This is not to say, not at all, that one cannot bring to one’s scholarship a fully engaged normative and ethical commitment, especially in the field of law which has, or should have, at its roots a commitment to justice. But it militates against careful listening, complex reasoning and understanding and more fine grained normative judgments. Justice is oftentimes not black and white.It is particularly so when it comes to dealing with the phenomenon of Populism which has moved from the fringe to the center of politics. Trying to understand Populism is not akin to justifying it.

Enlightening and Predicting the Correlation Around Deep Neural Nets and Cognitive Perceptions

Recently, psychologist has experienced drastic development using statistical methods to analyze the interactions of humans. The intention of past decades of psychological studies is to model how individuals learn elements and types. The scientific validation of such studies is often based on straightforward illustrations of artificial stimuli. Recently, in activities such as recognizing items in natural pictures, strong neural networks have reached or exceeded human precision. In this paper, we present Relevance Networks (RNs) as a basic plug-and-play application with Covolutionary Neural Network (CNN) to address issues that are essentially related to reasoning. Thus our proposed network performs visual answering the questions, superhuman performance and text based answering. All of these have been accomplished by complex reasoning on diverse physical systems. Thus, by simply increasing convolutions, (Long Short Term Memory) LSTMs, and (Multi-Layer Perceptron) MLPs with RNs, we can remove the computational burden from network components that are unsuitable for handling relational reasoning, reduce the overall complexity of the network, and gain a general ability to reason about the relationships between entities and their properties.

Investigating a Learning Progression for Reasoning Practices of Geocognition Using GeoMapApp-Based Assessment

This study examined a case of GeoMapApp-based assessment to investigate a learning progression for middle school students’ understanding of geoscience content and geocognition (spatial, temporal, and retrospective reasoning and system thinking). A 2-year GeoMapApp-based assessment process was administered along with a double-round of the construct modeling approach. Based on the measurement of Rasch analysis, the geocognition learning progression was described in terms of data-driven level, meaning-acquiring level, knowledge-constructing level, and complex reasoning with geocognition level. The geocognition learning progression developed in this study had significant implications in terms of the progress variables integrating geoscientific reasoning practices with geoscience content and the perspective on learning with adopting work-with-it view. While this content is applicable in all regions, it is especially helpful for science teachers in the Asia-Pacific region to understand geocognition learning progressions to improve teaching and better support students to understand local geological environments in terms of plate tectonics.

What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge

Open-domain question answering (QA) involves many knowledge and reasoning challenges, but are successful QA models actually learning such knowledge when trained on benchmark QA tasks? We investigate this via several new diagnostic tasks probing whether multiple-choice QA models know definitions and taxonomic reasoning—two skills widespread in existing benchmarks and fundamental to more complex reasoning. We introduce a methodology for automatically building probe datasets from expert knowledge sources, allowing for systematic control and a comprehensive evaluation. We include ways to carefully control for artifacts that may arise during this process. Our evaluation confirms that transformer-based multiple-choice QA models are already predisposed to recognize certain types of structural linguistic knowledge. However, it also reveals a more nuanced picture: their performance notably degrades even with a slight increase in the number of “hops” in the underlying taxonomic hierarchy, and with more challenging distractor candidates. Further, existing models are far from perfect when assessed at the level of clusters of semantically connected probes, such as all hypernym questions about a single concept.