Abstract: Aims and Objectives/Purpose/Research Questions: This article proposes the Scalpel Model, a new model of third and additional language (L3/Ln) acquisition. The model aims to identify and examine what happens beyond the initial state of acquisition and what factors may influence change from one state of knowledge to another. Design/Methodology/Approach: The article briefly examines the currently proposed hypotheses and models and evaluates the existing evidence for their predictions. It highlights several cognitive and experiential factors affecting crosslinguistic influence that are not taken into account by the current models. These factors include: structural linguistic complexity, misleading input or lack of clear unambiguous evidence for some property or construction, construction frequency in the target L3, and prevalent language activation or use. Data and Analysis: Findings of recently published research are discussed to support of the Scalpel Model. In particular, findings of differential learnability of properties within the same groups of learners suggest that L1 or L2 transfer happens property by property and is influenced by diverse factors. Findings/Conclusions: The Scalpel Model explicitly argues that wholesale transfer of one of the previously acquired languages does not happen at the initial stages of acquisition because it is not necessary. It also argues that transfer can be from the L1 or the L2 or both, but it is not only facilitative. Originality: The new model increases the explanatory coverage of the current experimental findings on how the L3/Ln linguistic representations develop. Significance/Implications: The model emphasizes the importance of the cognitive, experiential and linguistic influences on the L3/Ln beyond transfer from the L1 or L2. Thus it aligns L3/Ln acquisition with current debates within L2 acquisition theory.

Abstract: The choice associated with words is a fundamental property of natural languages. It lies at the heart of quantitative linguistics, computational linguistics and language sciences more generally. Information theory gives us tools at hand to measure precisely the average amount of choice associated with words: the word entropy. Here, we use three parallel corpora, encompassing ca. 450 million words in 1916 texts and 1259 languages, to tackle some of the major conceptual and practical problems of word entropy estimation: dependence on text size, register, style and estimation method, as well as non-independence of words in co-text. We present two main findings: Firstly, word entropies display relatively narrow, unimodal distributions. There is no language in our sample with a unigram entropy of less than six bits/word. We argue that this is in line with information-theoretic models of communication. Languages are held in a narrow range by two fundamental pressures: word learnability and word expressivity, with a potential bias towards expressivity. Secondly, there is a strong linear relationship between unigram entropies and entropy rates. The entropy difference between words with and without co-textual information is narrowly distributed around ca. three bits/word. In other words, knowing the preceding text reduces the uncertainty of words by roughly the same amount across languages of the world.

Abstract: The aim of this review is to summarize, analyze various research studies and identify different research gaps regarding usability standards and models, usability evaluation methods, usability metric, usability at different phases of software development life cycle and application domains of usability. This systematic review of usability studies between 1990 and 2016 has been conducted and 150 studies are identified. We conclude that researchers have not reached at consensus w.r.t. software usability models. We identify that Efficiency, Effectiveness, Satisfaction, and Learnability are commonly addressed attributes in various existing software usability models and standards. Further, developers do not have sufficient knowledge to decide the appropriate usability evaluation method to use in given domain. On the contrary, Usability Testing, Heuristic Evaluation and Questionnaire are identified frequently used methods for usability evaluation. Our findings investigate different metrics and measurement approaches used for usability estimation. But, current methods for usability measurement in practice do not include all ISO and ANSI defined aspects of usability into a single metric. Although, we identify studies concerning the integration of usability and software engineering into a single framework with generalizable results, their practical implementation is still missing and significantly needed. Conversely, this study highlights the fact that around 71% of studies address usability related issues during Design-Phase of software development life cycle. At present, usability issues have been identified in various domains but around 33.82% of studies identify that usability evaluation approach is widely used in Web-Domain.

Abstract: Aims: Over the past decade in particular, formal linguistic work within L3 acquisition has concentrated on hypothesizing and empirically determining the source of transfer from previous languages—L1, L2 or both—in L3 grammatical representations. In view of the progressive concern with more advanced stages, we aim to show that focusing on L3 initial stages should be one continued priority of the field, even—or especially—if the field is ready to shift towards modeling L3 development and ultimate attainment. Approach: We argue that L3 learnability is significantly impacted by initial stages transfer, as such forms the basis of the initial L3 interlanguage. To illustrate our point, the insights from studies using initial and intermediary stages L3 data are discussed in light of developmental predictions that derive from the initial stages models. Conclusions: Despite a shared desire to understand the process of L3 acquisition in whole, inclusive of offering developmental L3 theories, we argue that the field does not yet have—although is ever closer to—the data basis needed to effectively do so. Originality: This article seeks to convince the readership for the need of conservatism in L3 acquisition theory building, whereby offering a framework on how and why we can most effectively build on the accumulated knowledge of the L3 initial stages in order to make significant, steady progress. Significance: The arguments exposed here are meant to provide an epistemological base for a tenable framework of formal approaches to L3 interlanguage development and, eventually, ultimate attainment.

Abstract: I introduce seven criteria for determining the validity of competing theories for the original function of language. I go on to present a novel explanation that meets all the criteria: language originally evolved to teach kin. I suggest that the use of symbols subsequently generated evolutionary feedback at two levels, in the form of self-modified selection pressures that favored structures in the mind that functioned to manipulate and use symbols with efficiency, and cultural selection on languages for learnability.

Abstract: In this paper, we are concerned with the learnability of nonlocal interaction kernels for first-order systems modeling certain social interactions, from observations of realizations of their dynamics. This paper is the first of a series on learnability of nonlocal interaction kernels and presents a variational approach to the problem. In particular, we assume here that the kernel to be learned is bounded and locally Lipschitz continuous and that the initial conditions of the systems are drawn identically and independently at random according to a given initial probability distribution. Then the minimization over a rather arbitrary sequence of (finite-dimensional) subspaces of a least square functional measuring the discrepancy from observed trajectories produces uniform approximations to the kernel on compact sets. The convergence result is obtained by combining mean-field limits, transport methods, and a Γ-convergence argument. A crucial condition for the learnability is a certain coercivity property of the least square functional, defined by the majorization of an L2-norm discrepancy to the kernel with respect to a probability measure, depending on the given initial probability distribution by suitable push forwards and transport maps. We illustrate the convergence result by means of several numerical experiments.

Abstract: Aims and research questions:This study aims to investigate second language (L2) learnability in article acquisition from a feature-based contrastive approach by examining L1-Korean speakers’ compre...

Abstract: The invisible nature of VUIs has been attributed to challenging discoverability with VUIs. Low discoverability often leads to learnability issues. Researchers have designed visual tools for VUIs to help users learn as they go. However, few have used adaptation to ensure that learnability with the help of these tools extends beyond initial use. We designed DiscoverCal, a calendar application designed using adaptive discovery tools to improve learnability in VUIs. In this paper, we identify key characteristics of existing discovery tools. We present our design of a VUI that adapts based on contextual relevance and user performance in order to extend learnability beyond initial use. We briefly discuss our user study design.

Abstract: We study the learnability problem concerning the dative alternations in English (Baker, 1979; Pinker, 1989). We consider how first language learners productively apply the double-object and to-dati...

Abstract: Purpose This paper aims to examine and test the moderating influence of the type of knowledge underlying work – known as the knowledge in practice (KIP) perspective – on the relationship between knowledge management (KM) activities and unit performance. KIP proposes that the knowledge underlying work varies according to two dimensions: tacitness and learnability. This theory proposes that aligning KM activities with tacitness and learnability results in increased performance. However, to the authors’ knowledge, there exists no direct empirical tests of these propositions outlined in KIP theory. This study examines the empirical support for the theoretical predictions outlined by KIP. Design/methodology/approach The study uses a multiple survey, multiple respondent survey design to measure KM activity sets, the tacitness and learnability involved in work contexts and unit performance. Regression analysis is used to test the hypotheses. Findings In line with previous research, the authors find support for a direct relationship between some KM activity sets and unit performance. Surprisingly, the authors did not find support for the predictions offered by KIP theory. Specifically, the degree of tacitness or learnability did not moderate the relationship between KM activity sets and unit performance. Research limitations/implications The lack of findings to support the moderating effects of tacitness and learnability on the relationship between KM activity sets and unit performance challenges the adequacy of existing formulations of KIP theory. The authors discuss several important future research directions to examine this puzzling finding. Practical implications This paper reinforces the suggestion that managers at all levels of organizations should engage in KM activities to increase performance. These findings also suggest that considering the type of knowledge underlying a unit’s work should not be a consideration in implementing KM activities. Originality/value This is the first study to empirically test a KIP perspective. That is, how the type of knowledge involved in work moderates the relationships between KM activity sets and unit performance.

Abstract: Effects of orthographic depth on orthographic learning ability were examined in 10- to 13-year-old children who learnt to read in similar orthographies differing in orthographic depth, defined as c

Abstract: Background: Mental health apps tend to be narrow in their functioning, with their focus mostly being on tracking, management, or psychoeducation. It is unclear what capability such apps have to facilitate a change in users, particularly in terms of learning key constructs relating to behavioral interventions. Thought Challenger (CBITs, Chicago) is a skill-building app that engages users in cognitive restructuring, a core component of cognitive therapy (CT) for depression. Objective: The purpose of this study was to evaluate the learnability and learning performance of users following initial use of Thought Challenger. Methods: Twenty adults completed in-lab usability testing of Thought Challenger, which comprised two interactions with the app. Learnability was measured via completion times, error rates, and psychologist ratings of user entries in the app; learning performance was measured via a test of CT knowledge and skills. Nonparametric tests were conducted to evaluate the difference between individuals with no or mild depression to those with moderate to severe depression, as well as differences in completion times and pre- and posttests. Results: Across the two interactions, the majority of completion times were found to be acceptable (5 min or less), with minimal errors (1.2%, 10/840) and successful completion of CT thought records. Furthermore, CT knowledge and skills significantly improved after the initial use of Thought Challenger (P=.009). Conclusions: The learning objectives for Thought Challenger during initial uses were successfully met in an evaluation with likely end users. The findings therefore suggest that apps are capable of providing users with opportunities for learning of intervention skills. [JMIR Hum Factors 2017;4(3):e18]

Abstract: Learning management systems (LMS) is an integrated platform used in colleges and universities for distribution of educational materials and facilitate learning servicing various end users i.e. students, teachers and administration. The approach to learning significantly differs from one program to another especially in Engineering. In assessing the effectiveness of LMS, variables considered in the model primarily tap elements critical to system design of a Learning Management System, which investigate the pedagogical approach, usability and user-interface satisfaction aspect. The result showed that LMS was an effective tool to facilitate learning in an undergraduate engineering program in the Philippines because of its interactive environment and availability though it can be made more efficient by adding collaborative learning tools for students, which is deemed vital since engineering is a multidisciplinary and highly collaborative discipline.

Abstract: In this work we study the learnability of stochastic processes with respect to the conditional risk, i.e. the existence of a learning algorithm that improves its next-step performance with the amount of observed data. We introduce a notion of pairwise discrepancy between conditional distributions at different times steps and show how certain properties of these discrepancies can be used to construct a successful learning algorithm. Our main results are two theorems that establish criteria for learnability for many classes of stochastic processes, including all special cases studied previously in the literature.

Abstract: This research as a preliminary study to know the utilization of iJateng application through android. The purpose is to explain the accessibility and usability of digital library in improving community knowledge, and to identify the factors that become obstacles in the utilization of Jateng. The research method used qualitative. Data were obtained using interview technique and document search. Utilization is described using accessibility and usability parameters. The result of this research concludes that accessibility to openness aspect has not been fully socialized to society. Furthermore, for usability on the aspect of learnability, it is still necessary assistance from the library manager to reduce the obstacles that arise when people use iJateng applications through smartphones. Factors that become obstacles in the utilization of digital library iJateng, such as: Internet network problems, incomplete digital collections, eyestrain, lack of socialization, and lack of readers to solve problems that arise when accessing information through the application iJateng.

Abstract: Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.

Abstract: The choice associated with words is a fundamental property of natural languages. It lies at the heart of quantitative linguistics, computational linguistics and language sciences more generally. Information theory gives us tools at hand to measure precisely the average amount of choice associated with words: the word entropy. Here, we use three parallel corpora, encompassing ca. 450 million words in 1916 texts and 1259 languages, to tackle some of the major conceptual and practical problems of word entropy estimation: dependence on text size, register, style and estimation method, as well as non-independence of words in co-text. We present two main findings: Firstly, word entropies display relatively narrow, unimodal distributions. There is no language in our sample with a unigram entropy of less than six bits/word. We argue that this is in line with information-theoretic models of communication. Languages are held in a narrow range by two fundamental pressures: word learnability and word expressivity, with a potential bias towards expressivity. Secondly, there is a strong linear relationship between unigram entropies and entropy rates. The entropy difference between words with and without co-textual information is narrowly distributed around ca. three bits/word. In other words, knowing the preceding text reduces the uncertainty of words by roughly the same amount across languages of the world.

Abstract: Context: Availability of multiple use case templates to document software requirements inevitably requires their characterization in terms of their relevance, usefulness, and the degree of the formality of the expressions. Objective: This paper reports two experimental studies that separately investigate two usability aspects, namely the comprehension and the learnability of use case templates for software specification problems. Method: We judged the comprehension aspect by evaluating the subjects’ understanding of the requirements, specified in eight different use case templates, and the ease with which the changes were made by them in the requirement specifications. The learnability aspect was judged by assessing the completeness, the correctness, and the redundancy of the use case specifications developed by the subjects using these eight use case templates for three software specification problems. Results: Our results suggested that the Kettenis’s use case template was found to be significantly more understandable, and the templates by Tiwari, Yue and Some were found to be significantly more flexible to adapt to the changes. On the learnability aspect, the way we formulated it, we found different templates to be more complete ( Kettenis ), correct ( Some ), and non-redundant ( Tiwari ). Conclusion: The specifications documented using a more detailed use case template with an intermediate degree of formality can be more comprehensible and flexible to adapt to the required changes to be made in the specification. A more formal template seems to enhance the learnability as well.

Abstract: In the problem of learning with label proportions, which we call LLP learning, the training data is unlabeled, and only the proportions of examples receiving each label are given. The goal is to learn a hypothesis that predicts the proportions of labels on the distribution underlying the sample. This model of learning is applicable to a wide variety of settings, including predicting the number of votes for candidates in political elections from polls. In this paper, we formally define this class and resolve foundational questions regarding the computational complexity of LLP and characterize its relationship to PAC learning. Among our results, we show, perhaps surprisingly, that for finite VC classes what can be efficiently LLP learned is a strict subset of what can be leaned efficiently in PAC, under standard complexity assumptions. We also show that there exist classes of functions whose learnability in LLP is independent of ZFC, the standard set theoretic axioms. This implies that LLP learning cannot be easily characterized (like PAC by VC dimension).

Abstract: It has long been recognized that there is a natural dependence between theories of knowledge representation and theories of knowledge acquisition, with the idea that the right knowledge representation enables acquisition to happen as reliably as it does. Given this, a reasonable criterion for a theory of knowledge representation is that it be useful for acquisition, particularly in nontrivial learning situations. We propose quantitative learnability metrics meant to capture how useful a representation is for acquisition. We then apply these metrics to the case study of English metrical stress, a language that is notorious for having nonproductive aspects in its grammar and so being nontrivial to learn a productive grammar for. We examine three theories of metrical stress representation, assessing their learnability via these metrics from English child-directed speech at different stages of linguistic development. We find that while all three theories are only somewhat useful at the initial stages ...

Abstract: The android based application "Jogja Smart Tourism (JST)" is designed to help everyone who visited Yogyakarta to enjoy their travel. As new application, it is need to be tested for its usability before launched. Usability testing will show how easy user interfaces are to used. The objective of this research is to demonstrate the result of usability testing for application JST based on five characteristics: learnability, effectiveness, memorability, errors, and satisfaction. About 30 respondents were involved to test the usability of this application. Learnability and effectiveness is calculated from some task that should be finished by respondents, and the rest aspects are calculated from questionnaires that should be answered after simulation. There are 14 functions bound in this usability testing. The result shows total usability level is in 81.75%. Learnability testing shows that 98.8% of respondent could finish the task successfully with 87.5% in efficiency. The memorability level of respondents is good (84.5%) where their ability to fix the errors is 71.5%. And the last for satisfaction level of application interface is 66.25%. Low level of satisfaction occurred because most of respondent felt uncomfortable with landscape interface of application because they should turn their mobile phone while using JST application and also it happened because the lack of using picture and colour inside the application. Both of these becomes important note for the improvement of further applications where the interface in a portrait version is more comfort the use and also utilization of colour and the image will be the main focus to improve customer satisfaction.

Abstract: We consider the following statistical estimation problem: given a family F of real valued functions over some domain X and an i.i.d. sample drawn from an unknown distribution P over X, find h in F such that the expectation of h w.r.t. P is probably approximately equal to the supremum over expectations on members of F. This Expectation Maximization (EMX) problem captures many well studied learning problems; in fact, it is equivalent to Vapnik's general setting of learning. Surprisingly, we show that the EMX learnability, as well as the learning rates of some basic class F, depend on the cardinality of the continuum and is therefore independent of the set theory ZFC axioms (that are widely accepted as a formalization of the notion of a mathematical proof). We focus on the case where the functions in F are Boolean, which generalizes classification problems. We study the interaction between the statistical sample complexity of F and its combinatorial structure. We introduce a new version of sample compression schemes and show that it characterizes EMX learnability for a wide family of classes. However, we show that for the class of finite subsets of the real line, the existence of such compression schemes is independent of set theory. We conclude that the learnability of that class with respect to the family of probability distributions of countable support is independent of the set theory ZFC axioms. We also explore the existence of a "VC-dimension-like" parameter that captures learnability in this setting. Our results imply that that there exist no "finitary" combinatorial parameter that characterizes EMX learnability in a way similar to the VC-dimension based characterization of binary valued classification problems.

Abstract: Background. The EMERGO method and online platform enable the development and delivery of scenario-based serious games that foster students to acquire professional competence. One of the main goals of the platform is to provide a user-friendly authoring environment for creating virtual environments where students can perform authentic tasks. Aim. We present the findings of an in-depth qualitative case study of the platform's authoring environment and compare our findings on usability with those found for comparable environments in literature. Method. We carried out semi-structured interviews, with two experienced game developers who have authored a game for higher education, and a literature review of comparable environments. Findings. The analysis shows that the usability of the authoring environment is problematic, especially regarding understandability and learnability, which is in line with findings of comparable environments. Other findings are that authoring is well integrated with the EMERGO method and that functionality and reliability of the authoring environment are valued. Practical implications. The lessons learned are presented in the form of general guidelines to improve the understandability and learnability of authoring environments for serious games.

Abstract: We study mechanisms to characterize how the asymptotic convergence of backpropagation in deep architectures, in general, is related to the network structure, and how it may be influenced by other design choices including activation type, denoising and dropout rate. We seek to analyze whether network architecture and input data statistics may guide the choices of learning parameters and vice versa. Given the broad applicability of deep architectures, this issue is interesting both from theoretical and a practical standpoint. Using properties of general nonconvex objectives (with first-order information), we first build the association between structural, distributional and learnability aspects of the network vis-a-vis their interaction with parameter convergence rates. We identify a nice relationship between feature denoising and dropout, and construct families of networks that achieve the same level of convergence. We then derive a workflow that provides systematic guidance regarding the choice of network sizes and learning parameters often mediated4 by input statistics. Our technical results are corroborated by an extensive set of evaluations, presented in this paper as well as independent empirical observations reported by other groups. We also perform experiments showing the practical implications of our framework for choosing the best fully-connected design for a given problem.

Abstract: One of software quality criteria that is vital to determine the success of a software system is usability (ISO/IEC 9126-1:2001), also known as operability (ISO/IEC 25010:2011). There are a few sub-criteria that support operability and two of them are attractiveness and learnability. There is still lack of systematic review with regard to usability or operability with the focus on attractiveness and learnability mainly in Web applications. As more software systems nowadays are webbased, studying these quality factors are indeed essential. This study adopts a systematic literature review method to investigate existing works on the two sub-criteria besides exploring the works in both usability and operability in Web applications in general. The results specifically examine the issues, strengths and weaknesses that also conclude the gaps in existing works on attractiveness and learnability in Web applications besides the focus on existing frameworks.

Abstract: Topological models of empirical and formal inquiry are increasingly prevalent. They have emerged in such diverse fields as domain theory [1, 16], formal learning theory [18], epistemology and philosophy of science [10, 15, 8, 9, 2], statistics [6, 7] and modal logic [17, 4]. In those applications, open sets are typically interpreted as hypotheses deductively verifiable by true propositional information that rules out relevant possibilities. However, in statistical data analysis, one routinely receives random samples logically compatible with every statistical hypothesis. We bridge the gap between propositional and statistical data by solving for the unique topology on probability measures in which the open sets are exactly the statistically verifiable hypotheses. Furthermore, we extend that result to a topological characterization of learnability in the limit from statistical data.