Learning by stochastic serializations

Complex structures are typical in machine learning. Tailoring learning algorithms for every structure requires an effort that may be saved by defining a generic learning procedure adaptive to any complex structure. In this paper, we propose to map any complex structure onto a generic form, called serialization, over which we can apply any sequence-based density estimator. We then show how to transfer the learned density back onto the space of original structures. To expose the learning procedure to the structural particularities of the original structures, we take care that the serializations reflect accurately the structures' properties. Enumerating all serializations is infeasible. We propose an effective way to sample representative serializations from the complete set of serializations which preserves the statistics of the complete set. Our method is competitive or better than state of the art learning algorithms that have been specifically designed for given structures. In addition, since the serialization involves sampling from a combinatorial process it provides considerable protection from overfitting, which we clearly demonstrate on a number of experiments.Comment: Submission to NeurIPS 201

Concept and Design Developments in School Improvement Research

This open access book discusses challenges in school improvement research and different methodological approaches that have the potential to foster school improvement research. Research on school improvement and accountability analysis places high demands on a study’s design and method. The potential of combining the depth of case studies with the breath of quantitative measures and analyses in a mixed-methods design seems very promising. Consequently, the focus of the book lies on innovative methodological approaches. The book chapters address design, measurement, and analysis developments as well as theoretical and conceptual developments. The relevance of the research presented in the chapters for educational accountability is discussed in the book’s discussion chapter. More specifically, authors present one specific innovative methodological approach and clarify that approach with a concrete example in the context of school improvement, based on empirical data when possible. In this way, this book helps researchers designing complex useful studies

INVALSI data: assessments on teaching and methodologies

The school system has always aimed to achieve quality teaching, which is able, on the one hand, to give adequate responses to the expectations of all the stakeholders and, on the other, to introduce tools, actions, and checks through which the training offer can be constantly improved. This process is undoubtedly linked to scientific research. Researchers and Academics start from the data available to them or collect new ones, to discover and/or interpret facts and to find answers and new cues of reflection. A favorable environment for this work was the Seminar “INVALSI data: a research and educational teaching tool”, in its fourth edition in November 2019. The volume consists of six chapters, which are arise within the aforementioned Seminar context and, while dealing with heterogeneous topics, offer important examples of research both on teaching and on the methodologies applied to it. As a Statistical Service, which for years has taken care of the collection and dissemination of data, we hope that in this, as in the other volumes of the series, the reader will find confirmation of the importance that data play, both in scientific research and in practice in classroom

Next Generation of Product Search and Discovery

Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is information intensive. One of the key factors to succeed in e-business is how to facilitate the consumers’ approaches to discover a product. Conventionally a product search engine based on a keyword search or category browser is provided to help users find the product information they need. The general goal of a product search system is to enable users to quickly locate information of interest and to minimize users’ efforts in search and navigation. In this process human factors play a significant role. Finding product information could be a tricky task and may require an intelligent use of search engines, and a non-trivial navigation of multilayer categories. Searching for useful product information can be frustrating for many users, especially those inexperienced users. This dissertation focuses on developing a new visual product search system that effectively extracts the properties of unstructured products, and presents the possible items of attraction to users so that the users can quickly locate the ones they would be most likely interested in. We designed and developed a feature extraction algorithm that retains product color and local pattern features, and the experimental evaluation on the benchmark dataset demonstrated that it is robust against common geometric and photometric visual distortions. Besides, instead of ignoring product text information, we investigated and developed a ranking model learned via a unified probabilistic hypergraph that is capable of capturing correlations among product visual content and textual content. Moreover, we proposed and designed a fuzzy hierarchical co-clustering algorithm for the collaborative filtering product recommendation. Via this method, users can be automatically grouped into different interest communities based on their behaviors. Then, a customized recommendation can be performed according to these implicitly detected relations. In summary, the developed search system performs much better in a visual unstructured product search when compared with state-of-art approaches. With the comprehensive ranking scheme and the collaborative filtering recommendation module, the user’s overhead in locating the information of value is reduced, and the user’s experience of seeking for useful product information is optimized

INVALSI data: assessments on teaching and methodologies

The school system has always aimed to achieve quality teaching, which is able, on the one hand, to give adequate responses to the expectations of all the stakeholders and, on the other, to introduce tools, actions, and checks through which the training offer can be constantly improved. This process is undoubtedly linked to scientific research. Researchers and Academics start from the data available to them or collect new ones, to discover and/or interpret facts and to find answers and new cues of reflection. A favorable environment for this work was the Seminar “INVALSI data: a research and educational teaching tool”, in its fourth edition in November 2019. The volume consists of six chapters, which are arise within the aforementioned Seminar context and, while dealing with heterogeneous topics, offer important examples of research both on teaching and on the methodologies applied to it. As a Statistical Service, which for years has taken care of the collection and dissemination of data, we hope that in this, as in the other volumes of the series, the reader will find confirmation of the importance that data play, both in scientific research and in practice in classroom

The Assessment, Moderated Mediating Effects, and Influencing Factors of Critical Thinking Disposition in Chinese Undergraduate Students

The complexity and importance of critical thinking (CT) are prompting scholars to continue exploring its framework and application, particularly in this smart and globalized world. Although most nations have integrated CT into their higher education curriculum and assessments, China has not explicitly done that. The CT framework with both critical thinking skills (CTS) and disposition (CTD) is consistent, but CTD research has received less attention than the research on CTS. However, CTD is regarded as the activation of CTS, and CTS cannot be acquired adequately without CTD. Besides, the previous CTD frameworks and assessments neglected the perspectives of the workforce, which has aroused a mismatch between students’ outcomes and labor market demands. Accordingly, the goals of this dissertation are to (a) establish a comprehensive CT framework including problem-solving skills with the support of the effectiveness of problem-based learning (PBL) on CT instructional intervention via a review of literature by meta-analysis, (b) develop a CTD inventory integrating workforce intelligence to eliminate the required competences gap between schools and the job market, (c) explore the connections between each CTD component and the gender role in these interactions in order to achieve balance and equality, and (d) identify the influencing factors in terms of demographic characteristics for the attention paid by parents and instructors. Six chapters will be listed in this dissertation, with the first (Chapter 1) and last (Chapter 6) being the general introduction, discussions, and conclusions of this dissertation, respectively, and the main four chapters (Chapters 2 to 5) being the independent studies within the dissertation topic. The first study in Chapter 2 aims to identify the effectiveness and moderators of CTS and CTD cultivation based on PBL by conducting a meta-analysis of 50 relevant empirical studies from 2000 to 2021 with 5,210 participants and 58 effect sizes. The results showed that PBL was beneficial in promoting overall CT, CTS, and CTD, demonstrating a favorable link between them. The moderators are maturity, nationality, instruction type, group size, and types of sample selection for CT, maturity, nationality, instruments, discipline, group size, and intervention duration for CTS, and sample type, instruction type, discipline, and treatment duration for CTD. The goal of the second study in Chapter 3 is to create and validate the Employer-Employee-Supported Critical Thinking Disposition Instrument (2ES-CTDI) for assessing CTD in college students. To synthesize ideas from literature, 25 employers, 43 workers, and a six-person expert panel, intelligence accumulation was used. The potential inventory was created using the interpretive structural model. In 2021, validation data were obtained from 661 undergraduate students from China (328 males, 49.6%, Mean = 19.22, SD = 1.20 and 333 females, 50.4%, Mean = 19.91, SD = 1.23). Exploratory factor analysis was used to assess the consistency of the conceptual framework and data, confirmative factor analysis and the common method variance were used to assess model fit and structural validity, partial least squares structural equation modeling (PLS-SEM) was used to identify mediating effects among CTD components, and item response theory was used to assess test endorsement and item discrimination. Eventually, a seven-point CT disposition inventory with three factors was confirmed with good reliability, validity, and item and test endorsement for future use. The purpose of the third study in Chapter 4 is to examine the gender equality of 2ES-CTDI as well as the moderated mediating effects of gender on the components of CTD in 661 Chinese undergraduates using measurement invariance (MI) and PLS-SEM. The findings revealed that (a) the scale has great reliability and validity. MI findings revealed that the configural and metric models were met, and the scalar model identified the partial invariance; (b) Females have higher self-efficacy and habitual truth-digging dispositions, whereas males have higher instant judgment; (c) Instant judgment has a negative influence on habitual truth-digging, with self-efficacy as the competitive partial mediator, in which gender moderated the relations. Using dummy variable regression on the plausible values of 661 Chinese undergraduate students, the fourth study in Chapter 5 intends to investigate the determining elements of undergraduate students’ CTD from the standpoint of demographic information. The findings revealed that (a) differences in CTD may exist between public and private universities, grades, parents’ educational levels, and family incomes when considered separately; (b) however, when integrated into a comprehensive robust regression model, family income was identified as the unique factor influencing CTD, and those from the highest income family have a higher CTD. Notwithstanding the dissertation’s successful d