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The purpose of this paper is to identify men’s clothing market segments based on store types and generational cohorts and the retail attributes.

A total of 2,808 US male consumer data from Predictive Analytics survey were analyzed with correspondence analysis (CA) (to identify segments based on store types and generations), general linear model (GLM) (to determine what retail attributes were important to target each segment) and a Rasch tree model (to test items of each factor in their relative importance).

The CA produced three segments: Segment 1 (Gen Y male consumers who frequently shop at specialty stores), Segment 2 (Gen X males who frequently shop at discount stores and online stores) and Segment 3 (Baby Boomers and Seniors who frequently shop at department stores). GLM shows that fundamentals were important to all segments; experiential was most important to Segment 1, while promotion was most important to Segment 3. Rasch tree analysis provided specific information on retail attributes for each store type and each generation.

Future research could employ both the importance and performance of retail attributes that are measured on a rating scale to understand consumers’ attitudes toward each retail format.

This study provided men’s clothing retailers with current insights into the male consumer segments based upon generational cohorts and store types from which they can better develop appropriate positioning strategies to satisfy the needs of each segment.

This study addressed the men’s clothing market, a growing but largely ignored market in the clothing industry and the retail literature.

The paper aims to develop an evaluation model of the customer satisfaction index (CSI) in an R&D organization. A conceptual framework on customer satisfaction with a probabilistic approach has been attempted based on customer requirements and expectations in compliance with the clauses of ISO 9001:2008.

A survey through a well‐designed customer feedback data sheet has been used as an effective tool for the measurement of CSI. The questionnaire was framed on the basis of the requirements of a quality management system with advice to the customer for allotting grade points on a given scale to the quality parameters. The research model has been analyzed based on a fault‐tree approach and the probability of failure of each quality parameter has been assigned on the basis of grade point average. Data analysis for the estimation of the probability of failure at a customer satisfaction level (CSL) was carried out based on the probability of failure of each quality element graded by the customers. The data were also tested through statistical inference of whether customer‐to‐customer satisfaction level differs or not.

As a result of case study analysis, 88 percent of customers are fully satisfied. This gives significant information to the management process as well as providing a guiding tool for future improvements. The analysis was carried out based on a framed questionnaire graded by the customer and the result reveals that there is no significant difference between customer satisfaction levels.

This model can be used by any organization, irrespective of the number of customers participating, as well as the number of quality parameters being assigned in the customer feedback analysis.

A literature review found that there are various approaches for evaluating a CSI. The paper describes how a newly‐applied conceptual model based on the failure of CSL in the form of a fault‐tree approach was designed and how the probability of failure of each element/parameters was assigned on the basis of a grade point average to evaluate the CSI, as well as the variation in satisfaction levels between customers being analyzed.

Increased interest in team dynamics has resulted in new methods for measuring teamwork over time. The primary purpose of this chapter is to provide a survey of recent developments in teamwork/collaboration measurement in an educational context. Key topics include conceptual frameworks, large-scale assessments, and innovative measurement techniques.

A range of methods for collecting and analyzing teamwork data are discussed, and five frameworks for measuring collaborative problem solving (CPS) over time are compared. Frameworks from Programme for International Student Assessment (PISA), Assessment and Teaching of 21st Century Skills (ATC21S) project, Educational Testing Service (ETS), ACT, and von Davier and Halpin (2013) are discussed. Results of assessments developed from these frameworks are also considered.

New techniques for measuring team dynamics over time have great potential to improve education and work outcomes. Preliminary results of the assessments developed from these frameworks show that important advances in teamwork measurement have been enabled by innovative task designs, data-mining techniques, and novel applications of stochastic models.

This novel overview and comparison of interdisciplinary approaches will help to indicate where progress has been made and what challenges are ahead.

Surveys that include skill measures may suffer from additional sources of error compared to those containing questionnaires alone. Examples are distractions such as noise or interruptions of testing sessions, as well as fatigue or lack of motivation to succeed. This paper aims to provide a review of statistical tools based on latent variable modeling approaches extended by explanatory variables that allow detection of survey errors in skill surveys.

This paper reviews psychometric methods for detecting sources of error in cognitive assessments and questionnaires. Aside from traditional item responses, new sources of data in computer-based assessment are available – timing data from the Programme for the International Assessment of Adult Competencies (PIAAC) and data from questionnaires – to help detect survey errors.

Some unexpected results are reported. Respondents who tend to use response sets have lower expected values on PIAAC literacy scales, even after controlling for scores on the skill-use scale that was used to derive the response tendency.

The use of new sources of data, such as timing and log-file or process data information, provides new avenues to detect response errors. It demonstrates that large data collections need to better utilize available information and that integration of assessment, modeling and substantive theory needs to be taken more seriously.

This study aims to describe the behavior of blocks in the system under consideration using systems modeling language (SysML) state machine diagrams. In this paper, formalization and model checking for SysML state machine diagrams have been investigated.

The work by Zhang and Liu (2010) proposed a formalization of SysML state machine diagrams in which the diagrams were translated into CSP# processes that could be verified by the state-of-the-art model checker PAT. In this paper, several modifications have been made and new rules have been added to the translation described in that work.

First, three translation rules were modified, which apparently are inappropriately defined according to the SysML definition of state machine diagrams. Next, we add new translation rules for two components of the diagrams – junction and choice pseudostates – which have not been dealt with previously. Further, we are implementing the automatic translation system on a web-based model-driven development tool, which reflects on our translation rules.

As the contribution of this work, more reasonable verification results for more general SysML state machine diagrams can be achieved.

E-learning is part of instructional design and has opened a whole world of new possibilities in terms of learning and teaching. The purpose of this paper is to develop an adaptive e-learning platform that enhances skills from primary school to university learners. Two purposes converge here: a pedagogical one – offering new possibilities, especially in terms of teaching scenarios (blended learning); and a research one – confirming the effectiveness of an adaptive e-learning tool in the case of individualized cross-disciplinary competences, such as comprehension of implicit information in written texts (French).

The case study presented here concerns primary-school learners using the Implicit module of TACIT adaptive e-learning tool over the 2016-2017 academic year.

This paper gives a first positive answer to the effectiveness of such a tool in this specific context. This pedagogical effectiveness is more pronounced for low-level pupils, especially for girls and for older pupils (CM1/CM2, respectively, fourth/fifth grade).

In this case study, the module comes from an existing platform, created by the TACIT research group. The adaptive environment was created by using the Item Response Theory models and, more precisely, the Rasch model.

The potential of a probabilistic developmental model of leadership with a possible vast array of organisational applications, suggested by a recent study, is evaluated in light of the historical development of the leadership construct and to suggest directions for the future. The historical development of leadership theories is explored, the model presented, and its potential and limitations discussed.

The purpose of this study is to explore mathematics teachers' collaboration within their professional learning communities (PLCs), and to gain insight into how the teachers developed their collective efficacy through it to focus on improving student learning.

Using a qualitative approach, data were collected from three high-performing secondary schools, mainly through semi-structured in-depth interviews with the Heads of Mathematics Unit. For explication and triangulation purposes, focus group interviews with the mathematics teachers and students and document analysis were also conducted.

The findings indicate that PLCs were the vessel through which a culture of disciplined teacher collaboration was fostered. More importantly, this culture encouraged teachers to participate in reflective dialogues that became a driver for their learning, and eventually led to their changed professional practice for improved student learning. The collaborative setting within the PLCs allowed teachers to pool their resources, knowledge and individual expertise to identify their student learning focus, and administer and measure their student intervention strategies. This collaborative professional learning reduced professional isolation, and as a result, improved teachers' collective pursuit of professional growth to impact student outcomes.

The aim of this qualitative study was to provide a detailed description of the teachers' collaboration within their PLCs based on the researchers' interpretation of how the participants make sense of their PLC practices and experiences. Hence, findings are indicative, and not definitive. In addition, the Heads of the Mathematics Unit, and the mathematics teacher participants in this study illustrated salient aspects of subject subcultures, where their beliefs, norms and practices were non-generic, or specific only to their subject context. Therefore, the implications to practice were built from commonalities shared only among the mathematics units.

This is a study in a Malaysian context that provides empirical evidences that are concerned primarily with the process of teachers developing their collective efficacy through disciplined collaboration within their PLCs.