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Excerpted from my book Generally Speaking, this paper introduces "concept‐driven sociology," a special way of theorizing designed to reveal abstract social patterns. As such, it examines the methodological process by which we can "distill" generic patterns from the culturally, historically, and situationally specific contexts in which we encounter them. It thus champions a "generic sociology" that is pronouncedly transcontextual (transcultural, transhistorical, transsituational, and translevel) in its scope. In order to uncover generic, transcontextual social patterns, we need to collect our data in a wide range of social contexts. Such contextual diversity is manifested multi‐culturally, multihistorically, multisituationally, as well as at multiple levels of social aggregation. True to its message, the book illustrates generic social patterns by drawing on numerous examples from diverse cultural contexts and historical periods and a wide range of diverse social domains, as well as by disregarding scale. Emphasizing cross‐contextual commonality, concept‐driven sociology tries to reveal formal "parallels" across seemingly disparate contexts. The paper features the four main types of cross‐contextual analogies concept‐driven sociologists tend to use—cross‐cultural, cross‐historical, cross‐domain, as well as cross‐level—disregarding conventionally noted substantive differences in order to note conventionally disregarded formal equivalences. [ABSTRACT FROM AUTHOR]

This paper develops a three-layer model and elastic solutions to capture nonlinear response of rigid, passive piles in sliding soil. Elastic solutions are obtained for an equivalent force per unit length ps of the soil movement. They are repeated for a series of linearly increasing ps (with depth) to yield the nonlinear response. The parameters underpinning the model are determined against pertinent numerical solutions and model tests on passive free-head and capped piles. The solutions are presented in non-dimensional charts and elaborated through three examples. The study reveals the following: On-pile pressure in rotationally restrained, sliding layer reduces by a factor α, which resembles the p-multiplier for a laterally loaded, capped pile, but for its increase with vertical loading (embankment surcharge), and stiffness of underlying stiff layer: α = 0.25 and 0.6 for a shallow, translating and rotating piles, respectively; α = 0.33-0.5 and 0.8-1.3 for a slide overlying a stiff layer concerning a uniform and a linearly increasing pressure, respectively; and α = 0.5-0.72 for moving clay under embankment loading., Ultimate state is well defined using the ratio of passive earth pressure coefficient over that of active earth pressure. The subgrade modulus for a large soil movement may be scaled from model tests., The normalised rotational stiffness is equal to 0.1-0.15 for the capped piles, which increases the pile displacement with depth., The three-layer model solutions well predict nonlinear response of capped piles subjected to passive loading, which may be used for pertinent design. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

When teaching a novel mathematical concept, should we present learners with abstract or concrete examples? In this experiment, we conduct a critical replication and extension of a well‐known study that argued for the general advantage of abstract examples (Kaminski, Sloutsky, & Heckler, 2008a). We demonstrate that theoretically motivated yet minor modifications of the learning design put this argument in question. A key finding from this study is that participants who trained with improved concrete examples performed as well as, or better than, participants who trained with abstract examples. We argue that the previously reported "advantage of abstract examples" manifested not because abstract examples are advantageous in general, but because the concrete condition employed suboptimal examples. [ABSTRACT FROM AUTHOR]

Abstract This papers describes an exploratory study into the early phase of getting to know end-user software during which users make use of a variety of information resources, including the user interface/program itself, manuals, on-line help, examples provided in the manuals and other sources. In particular, how do novices make use of the worked-out examples often provided in manuals and during training? Building on earlier research on the self-explanation effect, thinking aloud data from 10 participants were analysed to see how examples were studied and how they were used during problem solving. Important effects of self-explaining comparable to findings in other domains were found in this study. For instance, those participants who self-explain with the goal to discover meaning prove to be better problem solvers than those who do not self-explain or who focus more on syntactical aspects of examples. [ABSTRACT FROM AUTHOR]

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools and found that their effectiveness was determined by whether they uniquely pointed to the correct rule. Experiment 3 compared verbal directions with examples and found that both could guide search. The final experiment tried to improve learning by using more explicit verbal directions or by adding scaffolding to the example. While both manipulations improved learning, learning still took the form of a search through a hypothesis space of possible rules. We describe a model that embodies two assumptions: (1) the instruction can bias the rules participants hypothesize rather than directly be encoded into a rule; (2) participants do not have memory for past wrong hypotheses and are likely to retry them. These assumptions are realized in a Markov model that fits all the data by estimating two sets of probabilities. First, the learning condition induced one set of Start probabilities of trying various rules. Second, should this first hypothesis prove wrong, the learning condition induced a second set of Choice probabilities of considering various rules. These findings broaden our understanding of effective instruction and provide implications for instructional design. [ABSTRACT FROM AUTHOR]