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Informal Digital Learning of English (IDLE) refers to individual's learning endeavor occurring in out-of-class environments in a naturalistic way independent from the requirements of a formal education program including assessment. Learners' IDLE experiences in English as a Foreign Language (EFL) contexts are quite important to understand their motives, and the potential ways to improve such practices for enhanced language practice. This study aims to present IDLE practices of higher education Turkish EFL learners in detail and investigate the motivations of EFL learners considering their actual endeavors with digital language learning sources. For this purpose, a total of 115 students from an English Preparatory Program at a university in Turkiye took part in the study. Within a quantitative research design, the data were collected through a questionnaire developed for the purposes of the study based on rigorous research on IDLE literature to identify EFL learners' IDLE practices, their motivations on using digital sources for language practice, potential difficulties regarding such practices, and the role of guidance. The results of descriptive statistics indicated that the majority of the participants spent their time on IDLE practices mostly for using social networking platforms, music and song platforms, translation sites, video sharing sites, and series and movies on specific network platforms. Findings also showed that teacher's guidance had great importance on the participants' IDLE practices and learners needed systematic guidance in the vast world of digital sources for effective language practice. What is more, some obstacles hindering EFL learners' IDLE practices were identified such as the expenses related to digital sources (e.g. membership fees, internet costs, etc.) which echoed the reality of the digital divide. The findings of the study are quite important in understanding the need to use digital sources for learners' informal learning practices. This study has certain implications and suggestions for language practitioners and teachers to foster IDLE practices. [This article was presented at the 5th International Open & Distance Learning Conference (IODL 2022).]

Reflective practice is considered as an effective way for professional development in order to gain awareness of one's own teaching as well as to compete with the changing needs of the students. Especially in pre-service period, when pre-service teachers work cooperatively with their peers in a reciprocal fashion towards reflectivity, it has a potential to promote advancement in reflective practices and help them focus on the underlying meaning behind their actions. Based on these ideas, this study aimed at engaging pre-service teachers in a reflective reciprocal peer coaching experience and investigating whether such experience caused any changes in their reflectivity. For this purpose, 12 pre-service teachers in a Turkish ELT context participated in the study and a reflective reciprocal peer coaching program was implemented with a training aspect. In a mixed method study design, change in participants' reflectivity was measured with a profile of reflective thinking attributes scale quantitatively and data were supported qualitatively with reflective diaries, video recordings of post-conference sessions and focus-group interviews. Results of quantitative and qualitative analyses put forward that the pre-service teachers advanced in their reflectivity throughout the reflective reciprocal peer coaching practice and benefited much from this experience before they embark into professional life. This study provides valuable implications to use reflection embedded in a peer coaching program and offers suggestions for teacher educators

The present study was carried out with 46 teacher candidates taking the course of "Thermodynamics" in the Department of Physics Teaching. The purpose of the study was to determine the difficulties that teacher candidates experienced in explaining the heat, work and internal energy relationships in the processes of adiabatic compression and expansion of an ideal gas. By examining both the results the teacher candidates found and the related interpretations made by the teacher candidates, the difficulties they experienced in understanding the subject were determined. The difficulties determined were gathered under two categories: a) discriminating the concepts (heat, work, internal energy and temperature) and b) application of the first law of thermodynamics to the adiabatic processes. It was seen that most of the teacher candidates experienced difficulty in understanding the fact that there was no difference between the functions of the concepts of heat and work in the microscopic scale.

It is imperative to use new technologies in a supportive manner to meet the learners' and teachers' demanding needs as educational environments change in the digital age. The continuous expansion of online learning and distance education opportunities responds to the demands of learners and teachers while pioneering the use of technology in education. One advancement in English language teaching and learning in online environments, which assists teachers in reducing their workload and providing students with instant digital feedback, is the automated writing evaluation (AWE) tools. To gain a deeper understanding of the potential and limitations of these digital tools, this study aims to investigate the effectiveness of AWE feedback in error reduction in writing in English and the explore views of students regarding the utility of AWE tools. For this purpose, a total of 38 students at a university in Turkiye participated in the study, and three of their essays were evaluated. Within a concurrent triangulation mixed-method design, the changes in errors of the experimental group (n=18) receiving AWE feedback, and the control group (n=20) receiving teacher feedback were analyzed quantitatively, and the written reflection reports and semi-structured interviews conducted with the students were analyzed qualitatively. The results indicated that teacher feedback and AWE feedback were both effective in reducing errors in 11 categories. AWE feedback appeared to minimize errors in mechanics and usage more efficiently and teacher feedback was required more in content and organization issues. As a result, AWE was found as a complementary and effective tool supporting the improvement of target language writing skills saving time and energy for teachers. Furthermore, students expressed positive views regarding the use of AWE despite minor limitations. The findings of this study in general sheds light on using online digital tools of ubiquitous nature such as AWE to assist language improvement outside the class. [ABSTRACT FROM AUTHOR]

The aim of this study is to discover the effects of an e-class application on the computer and reading anxiety of learners, and the correlation between the two anxieties. The learners' perceptions of this new learning experience are also investigated. Information was obtained by using the Foreign Language Reading Anxiety Scale (FLRAS), the Computer Anxiety Scale (CAS) and learners' diaries. The study found that both anxieties decreased at the end of the application. A positive relationship between both types of anxiety was also identified. Students became more relaxed as they became more familiar with the new educational tool. They also displayed positive attitudes towards synchronous classes. This study demonstrates that anxiety free language learning can be created with interactive online facilities which are designed considering learners' needs and perceptions.

Informal Digital Learning of English (IDLE) refers to individual's learning endeavor occurring in out-of- class environments in a naturalistic way independent from the requirements of a formal education program including assessment. Learners' IDLE experiences in English as a Foreign Language (EFL) contexts are quite important to understand their motives, and the potential ways to improve such practices for enhanced language practice. This study aims to present IDLE practices of higher education Turkish EFL learners in detail and investigate the motivations of EFL learners considering their actual endeavors with digital language learning sources. For this purpose, a total of 115 students from an English Preparatory Program at a university in Turkiye took part in the study. Within a quantitative research design, the data were collected through a questionnaire developed for the purposes of the study based on rigorous research on IDLE literature to identify EFL learners' IDLE practices, their motivations on using digital sources for language practice, potential difficulties regarding such practices, and the role of guidance. The results of descriptive statistics indicated that the majority of the participants spent their time on IDLE practices mostly for using social networking platforms, music and song platforms, translation sites, video sharing sites, and series and movies on specific network platforms. Findings also showed that teacher's guidance had great importance on the participants' IDLE practices and learners needed systematic guidance in the vast world of digital sources for effective language practice. What is more, some obstacles hindering EFL learners' IDLE practices were identified such as the expenses related to digital sources (e.g. membership fees, internet costs, etc.) which echoed the reality of the digital divide. The findings of the study are quite important in understanding the need to use digital sources for learners' informal learning practices. This study has certain implications and suggestions for language practitioners and teachers to foster IDLE practices. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces a novel graph-based filter method for automatic feature selection (abbreviated as GB-AFS) for multi-class classification tasks. The method determines the minimum combination of features required to sustain prediction performance while maintaining complementary discriminating abilities between different classes. It does not require any user-defined parameters such as the number of features to select. The minimum number of features is selected using our newly developed Mean Simplified Silhouette (abbreviated as MSS) index, designed to evaluate the clustering results for the feature selection task. To illustrate the effectiveness and generality of the method, we applied the GB-AFS method using various combinations of statistical measures and dimensionality reduction techniques. The experimental results demonstrate the superior performance of the proposed GB-AFS over other filter-based techniques and automatic feature selection approaches, and demonstrate that the GB-AFS method is independent of the statistical measure or the dimensionality reduction technique chosen by the user. Moreover, the proposed method maintained the accuracy achieved when utilizing all features while using only 7– $$30\%$$ 30 % of the original features. This resulted in an average time saving ranging from $$15\%$$ 15 % for the smallest dataset to $$70\%$$ 70 % for the largest. Our code is available at https://github.com/davidlevinwork/gbfs/ .

Abstract The edges of stone tools have significant technological and functional implications. The nature of these edges–their sharpness, whether they are concave or convex, and their asymmetry–reflect how they were made and how they could be used. Similarly, blunt portions of a tool’s perimeter hint at how they could have been grasped or hafted and in which directions force could be applied. However, due to the difficulty in accurately measuring the complex 3D geometry of tool edges with traditional methods, their attributes are often overlooked. When they are analyzed, they have traditionally been assessed with visual qualitative categories or unreliable physical measurements. We introduce new computational 3D methods for automatically and repeatably measuring key attributes of stone tool edges. These methods allow us to automatically identify the 3D perimeter of tools, segment this perimeter according to changes in edge angles, and measure these discrete edge segments with a range of metrics. We test this new computational toolkit on a large sample of 3D models of handaxes from the later Acheulean of the southern Levant. Despite these handaxes being otherwise technologically and morphologically similar, we find marked differences in the amount of knapped outline, edge angle, and the concavity of their edges. We find many handaxes possess blunt portions of perimeter, suitable for grasping, and some handaxes even possess more than one discrete sharp edge. Among our sample, sites with longer occupations and more diverse toolkits possessed handaxes with more diverse edges. Above all, this paper offers new methods for computing the complex 3D geometry of stone tool edges that could be applied to any number of artifact types. These methods are fully automated, allowing the analysis and visualization of entire assemblages.

We design an adaptive learning algorithm for binary classification problems whose objective is to reduce the cost of misclassified instances derived from the consequences of errors. Our algorithm (Adaptive Cost-Sensitive Learning — AdaCSL) adaptively adjusts the loss function to bridge the difference between the class distributions between subgroups of samples in the training and validation data sets. This adjustment is made for samples with similar predicted probabilities, in such a way that the local cost decreases. This process usually leads to a reduction in cost when applied to the test data set (i.e., local training–test class distributions mismatch). We present empirical evidence that neural networks used with the proposed algorithm yield better cost results on several data sets compared to other approaches. In addition, the proposed AdaCSL algorithm can optimize evaluation metrics other than cost. We present an experiment that demonstrates how utilizing the AdaCSL algorithm generates superior accuracy results. The AdaCSL algorithm can be used for applications in which the training set is noisy or when large variability may occur between the training and validation data sets, such as the classification of disease severity for a given subject based on other subjects. Our code is available at https://github.com/OhadVolk/AdaCSL.

Spheroids are one of the least understood lithic items yet are one of the most enduring, spanning from the Oldowan to the Middle Palaeolithic. Why and how they were made remains highly debated. We seek to address whether spheroids represent unintentional by-products of percussive tasks or if they were intentionally knapped tools with specific manufacturing goals. We apply novel three-dimensional analysis methods, including spherical harmonics and surface curvature, to 150 limestone spheroids from ‘Ubeidiya (ca 1.4 Ma), presently the earliest Acheulean occurrence outside of Africa, to bring a new perspective to these enigmatic artefacts. We reconstruct the spheroid reduction sequence based on trends in their scar facets and geometry, finding that the spheroid makers at ‘Ubeidiya followed a premeditated reduction strategy. During their manufacture, the spheroids do not become smoother, but they become markedly more spherical. They approach an ideal sphere, a feat that likely required skilful knapping and a preconceived goal. Acheulean bifaces are currently thought to represent the earliest evidence of hominins imposing a premeditated, symmetrical shape on stone. The intentional production of sphere-like objects at ‘Ubeidiya similarly shows evidence of Acheulean hominins desiring and achieving intentional geometry and symmetry in stone.

We consider the problem of choosing the best of a set of sequential offers proposed by the market in a house-selling process. During each decision epoch, the seller sets a listing price, observes the offers and decides whether to accept the maximum one or to reject all of them. We model a fixed holding cost, which is the constant marketing cost of searching for buyers, and a variable cost that is proportional to the number of offers received during each epoch. The objective is to maximize the expected revenue. Most previous studies assume a stationary known distribution from which the buyers’ offers are generated and which reflects the market valuation of the house. In contrast, we assume that the number of incoming offers, and the distribution from which each individual offer is generated, are affected by the seller’s listing price (i.e., price-based demand response). Thus, we propose a new approach for the selling policy, which consists of the listing price and the offer acceptance threshold in each period. We derive the seller’s optimal selling policy and apply it to a scenario involving the sale of individual residential properties in Ames (Iowa), which yields results consistent with empirical observations.

Abstract Much of what is known about human behavior and subsistence strategies in the Levantine Middle Paleolithic comes from long sequences from caves and rock shelters. In this context, studies of stone tool function have traditionally focused on determining the use of Levallois points and triangular elements, either as projectiles or, more rarely, multipurpose knives. Little is known about such tool use and hafting in Middle Paleolithic open-air sites in the Levant through the systematic application of micro-wear analysis. Here we report the results of a low and high-power study performed on the lithic assemblage of the Late Middle Paleolithic open-air site of Nahal Mahanayeem Outlet (NMO, Israel). Most pointed items, including Levallois and non-Levallois points, were used as butchering knives, many of them while hafted; to a much lesser extent they were also used for hide, bone, and wood/plant processing activities. Blades and flakes were mostly handheld and used as butchering knives, with hide, bone, antler, and wood/plant-processing tasks being rare. Hafted artifacts include morphologies and activities for which hafting is not required, indicating that NMO inhabitants possessed varied hafting expertise. Wood/plant processing tools, some of which were hafted, attest that manufacture and maintenance tasks were planned well in advance of game procurement at the site. These results attest to early evidence of hafted butchering knives and hafted plant processing tools for a Late Middle Paleolithic open-air site in the Levant, and support previous interpretations of NMO as a short-term task-specific location focused on animal processing activities, mostly butchery.

Abstract Security and intelligence agencies around the world invest considerable resources in preventing terrorist attacks, as these may cause strategic damage, national demoralization, infringement of sovereignty, and government instability. Recently, data-mining techniques have evolved to allow identification of patterns and associations in criminal data that were not apparent using traditional analysis. The aim of this paper is to illustrate how to use interpretable classification algorithms to identify subgroups (“patterns”) of terrorist incidents that share common characteristics and that result in mass fatalities. This approach can produce insights far beyond those of conventional macro-level studies that use hypothesis-testing and regression models. In addition to this methodological contribution, from a practical perspective, exploring the characteristics identified in the “patterns” can lead to prevention strategies, such as alteration of the physical or systemic environment. This is in line with situational crime prevention (SCP) theory. We apply our methodology to the Global Terrorism Database (GTD). We present three examples in which terror attacks that are described by a particular pattern (set of characteristics) resulted in a high probability of mass casualties, while attacks that differ in just one of these characteristics (i.e., month of attack, geographical area targeted, or type of attack) resulted in far fewer casualties. We propose exploration of the differentiating characteristic as a means of reducing the probability of mass-fatality terrorist incidents.

Nineteen broken and complete bone fish hooks and six grooved stones recovered from the Epipaleolithic site of Jordan River Dureijat in the Hula Valley of Israel represent the largest collection of fishing technology from the Epipaleolithic and Paleolithic periods. Although Jordan River Dureijat was occupied throughout the Epipaleolithic (~20-10 kya the fish hooks appear only at the later stage of this period (15,000-12,000 cal BP). This paper presents a multidimensional study of the hooks, grooved stones, site context, and the fish assemblage from macro and micro perspectives following technological, use wear, residue and zooarchaeological approaches. The study of the fish hooks reveals significant variability in hook size, shape and feature type and provides the first evidence that several landmark innovations in fishing technology were already in use at this early date. These include inner and outer barbs, a variety of line attachment techniques including knobs, grooves and adhesives and some of the earliest evidence for artificial lures. Wear on the grooved stones is consistent with their use as sinkers while plant fibers recovered from the grooves of one hook shank and one stone suggest the use of fishing line. This together with associations between the grooved stones and hooks in the same archaeological layers, suggests the emergence of a sophisticated line and hook technology. The complexity of this technology is highlighted by the multiple steps required to manufacture each component and combine them into an integrated system. The appearance of such technology in the Levantine Epipaleolithic record reflects a deep knowledge of fish behavior and ecology. This coincides with significant larger-scale patterns in subsistence evolution, namely broad spectrum foraging, which is an important first signal of the beginning of the transition to agriculture in this region.

In this research, we develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used to select the classifying attributes. We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional daily growth factor of the spread of an epidemic based on a variety of explanatory factors. In such an application, some of the potential classification errors could have critical consequences. The classification tool will enable the spread of the epidemic to be tracked and controlled by yielding insights regarding the relationship between local containment measures and the daily growth factor. In order to benefit maximally from a variety of ordinal and non-ordinal algorithms, we also propose an ensemble majority voting approach to combine different algorithms into one model, thereby leveraging the strengths of each algorithm. We perform experiments in which the task is to classify the daily COVID-19 growth rate factor based on environmental factors and containment measures for 19 regions of Italy. We demonstrate that the ordinal algorithms outperform their non-ordinal counterparts with improvements in the range of 6–25% for a variety of common performance indices. The majority voting approach that combines ordinal and non-ordinal models yields a further improvement of between 3% and 10%.

The negative impact of absenteeism on organizations’ productivity and profitability is well established. To decrease absenteeism, it is imperative to understand its underlying causes and to identify susceptible employee subgroups. Most research studies apply hypotheses testing and regression models to identify features that are correlated with absenteeism—typically, these models are limited to finding simple correlations. We illustrate the use of interpretable classification algorithms for uncovering subgroups of employees with common characteristics and a similar level of absenteeism. This process may assist human resource managers in understanding the underlying reasons for absenteeism, which, in turn, could stimulate measures to decrease it. Our proposed methodology makes use of an objective-based information gain measure in conjunction with an ordinal CART model. Our results indicate that the ordinal CART model outperforms conventional classifiers and, more importantly, identifies patterns in the data that have not been revealed by other models. We demonstrate the importance of interpretability for human resource management through three examples. The main contributions of this research are (1) the development of an information-based ordinal classifier for a published absenteeism dataset and (2) the illustration of an interpretable approach that could be of considerable value in supporting human resource management decision-making.

The Intermediate Bronze Age (IB) in the Southern Levant (ca. 2350-2000 BCE) is known as the "Dark Ages," following the collapse of Early Bronze urban society and predating the establishment of the Middle Bronze cities. The absence of significant settlements and monumental building has led to the reconstruction of IB social organization as that of nomadic, tribal society inhabiting rural villages with no central governmental system. Excavation in the Shamir Dolmen Field (comprising over 400 dolmens) on the western foothills of the Golan Heights was carried out following the discovery of rock art engravings on the ceiling of the central chamber inside one of the largest dolmens ever recorded in the Levant. Excavation of this multi-chambered dolmen, covered by a basalt capstone weighing some 50 tons, revealed a secondary multi-burial (of both adults and children) rarely described in a dolmen context in the Golan. Engraved into the rock ceiling above the multi-burial is a panel of 14 forms composed of a vertical line and downturned arc motif. 3D-scanning by structured-light technology was used to sharpen the forms and revealed the technique employed to create them. Building of the Shamir dolmens required a tremendous amount of labor, architectural mastery, and complex socio-economic organization well beyond the capacity of small, rural nomadic groups. The monumental megalithic burial of the Shamir dolmens indicates a hierarchical, complex, non-urban governmental system. This new evidence supports a growing body of recent criticism stemming from new discoveries and approaches that calls for rethinking our views of the Levantine IB "Dark Ages."

Vehicle scanning methods are gaining popularity because of their ability to identify modal properties of several bridges with only one instrumentation setup, and several methods have been proposed in the last decade. In the numerical models used to develop and validate such methods, bridge damping is often overlooked, and its impact on the efficacy of vehicle scanning methods remains unknown. The present article addresses this knowledge gap by systematically investigating the effects of bridge damping on the efficacy of vehicle scanning methods in identifying the modal properties of bridges. For this, acceleration responses obtained from a numerical model of a bridge and vehicle are used. Four different scenarios are considered where vehicle damping, presence of road roughness, and traffic on the bridge are varied. Bridge damping is modeled using mass-proportional, stiffness-proportional, and Rayleigh damping models. The impacts of ignoring bridge damping or considering one of these damping models on the modal frequencies and mode shapes identified using the vehicle response are investigated by comparing the results. The outcomes of the numerical analysis show that ignoring bridge damping in vehicle scanning applications can significantly increase the efficacy of these methods. They also show that the identifiability of the bridge frequencies and bridge mode shapes from the vehicle response decreases significantly when bridge damping is considered. Further, the damping model used impacts which bridge modes can be identified because different damping models provide different modal damping ratios for each mode. The results highlight the importance of correctly simulating damping behavior of bridges, which is often ignored, to be able to correctly evaluate the efficacy of vehicle scanning methods, and they provide an important stepping stone for future studies in this field.