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Multidimensional achievement tests are recently gaining more importance in educational and psychological measurements. For example, multidimensional diagnostic tests can help students to determine which particular domain of knowledge they need to improve for better performance. To estimate the characteristics of candidate items (calibration) for future multidimensional achievement tests, we use optimal design theory. We generalize a previously developed exchange algorithm for optimal design computation to the multidimensional setting. We also develop an asymptotic theorem saying which item should be calibrated by examinees with extreme abilities. For several examples, we compute the optimal design numerically with the exchange algorithm. We see clear structures in these results and explain them using the asymptotic theorem. Moreover, we investigate the performance of the optimal design in a simulation study.

The purpose of this study was to identify kindergarteners at risk for language and reading disorders and to determine predictors. A representative sample of 311 kindergarteners in general education classrooms in the U.S. Midwest were assessed with the Well Screening in fall, winter, and spring. Groups were compared using analysis of variance (ANOVA) on measures of memory, phonological awareness, retrieval speed, social communication, and letter knowledge. Children at risk for reading disorders performed most poorly on phonological awareness, whereas children at risk for language disorders struggled most with social communication. Retrieval speed alone was not a reliable predictor of reading risk in kindergarten. High performers scored best on working memory. The high-performing reading group outscored the high-performing language group on letter knowledge. Phonological awareness distinguished the high-performing reading group but not the high-performing language group from their non-high-performing classmates. Early identification of these relevant subgroups allows for early targeted interventions and monitoring of growth.

In this paper, we develop a mixed stochastic approximation expectation-maximization (MSAEM) algorithm coupled with a Gibbs sampler to compute the marginalized maximum a posteriori estimate (MMAPE) of a confirmatory multidimensional four-parameter normal ogive (M4PNO) model. The proposed MSAEM algorithm not only has the computational advantages of the stochastic approximation expectation-maximization (SAEM) algorithm for multidimensional data, but it also alleviates the potential instability caused by label-switching, and then improved the estimation accuracy. Simulation studies are conducted to illustrate the good performance of the proposed MSAEM method, where MSAEM consistently performs better than SAEM and some other existing methods in multidimensional item response theory. Moreover, the proposed method is applied to a real data set from the 2018 Programme for International Student Assessment (PISA) to demonstrate the usefulness of the 4PNO model as well as MSAEM in practice.

The purpose of this study was to investigate multidimensional DIF with a simple and nonsimple structure in the context of multidimensional Graded Response Model (MGRM). This study examined and compared the performance of the IRT-LR and Wald test using MML-EM and MHRM estimation approaches with different test factors and test structures in simulation studies and applying real data sets. When the test structure included two dimensions, the IRT-LR (MML-EM) generally performed better than the Wald test and provided higher power rates. If the test included three dimensions, the methods provided similar performance in DIF detection. In contrast to these results, when the number of dimensions in the test was four, MML-EM estimation completely lost precision in estimating the nonuniform DIF, even with large sample sizes. The Wald with MHRM estimation approaches outperformed the Wald test (MML-EM) and IRT-LR (MML-EM). The Wald test had higher power rate and acceptable type I error rates for nonuniform DIF with the MHRM estimation approach. The small and/or unbalanced sample sizes, small DIF magnitudes, unequal ability distributions between groups, number of dimensions, estimation methods and test structure were evaluated as important test factors for detecting multidimensional DIF.

The US continues to transition toward a majority-minority composition and this trend has advanced most rapidly for school-aged children. Clinical work with diverse populations, and their families, calls for specialized skills and training experiences; however, these are not yet strongly reflected in most pre-service training programs in applied behavior analysis (ABA). A systematic review was conducted to evaluate the scope and range of the dimensions of diversity included in the behavior analytic literature (e.g., race/ethnicity, gender, sexuality) as well as to compile recommendations for improving culturally responsive practices in ABA (PROSPERO Registration: CRD42020181227). The search yielded a total of 58 unique publications that featured one or more dimensions of diversity. These works were summarized in terms of the dimension(s) of diversity featured, the goal(s) of the research, the types of demographics reported, and the recommendations put forth for improving culturally responsive practices in ABA. A reflection on the current state of diversity research in the field is provided and discussed in the context of current and future recommendations for culturally responsive behavior analysis.

Researchers in many disciplines work with ranking data. This data type is unique in that it is often deterministic in nature (the ranks of items "k"-1 determine the rank of item "k"), and the difference in a pair of rank scores separated by "k" units is equivalent regardless of the actual values of the two ranks in the pair. Given its unique qualities, there are specific statistical analyses and models designed for use with ranking data. The purpose of this manuscript is to demonstrate a strategy for analyzing ranking data from sample description through the modeling of relative ranks and inference regarding differences in ranking patterns between groups. An example dataset of university faculty ratings of job characteristics was used to demonstrate these various methods, and the ways in which they can be tied together to obtain a comprehensive understanding of a ranking dataset. The analyses were carried out using libraries from the R software package, and the code for this purpose is included in the appendix to the manuscript.

This study aims to analyze the differential bundle functioning in multidimensional tests with a specific purpose to detect this effect through differentiating the location of the item with DIF in the test, the correlation between the dimensions, the sample size, and the ratio of reference to focal group size. The first 10 items of the test that is comprised of 30 items were acknowledged as the bundle. The data in line with the parameters were generated via SAS program as two categories (1-0) and multidimensional through an extended 2PL model. Differential bundle functioning was detected via the SIBTEST procedure. The results of the study were interpreted according to the criteria of the power rate and the type I error. When the results were reviewed, the analysis of the bundle revealed that the more the correlation between the two dimensions increased, relatively the less the power rates became. It was observed that the power rates, which were obtained according to two different sample sizes in the study, increased as the sample size increased. Another result as to the SIBTEST's power for detecting DIF was the highest when the ratio of reference to focal group size was equal. According to the results of the type I error rate, the error rate was observed to be relatively decreasing as the correlation between the dimensions increased and it was observed to be increasing as the sample size increased. Also, the highest error rate was obtained when the ratio of the samples was equal.

This research aimed to explore the upper-secondary school chemistry teachers' and students' conceptual structures of atomic structure by using multidimensional scaling. Atomic structure is considered to be one of the most difficult concepts in upper-secondary school chemistry course so that the conceptual structure regarding atomic structure held by the learners is necessary to be examined. Based on a questionnaire survey for upper-secondary school chemistry teachers and university chemistry professors, 40 concepts were selected as a useful concept pool of atomic structure. Then, the conceptual structures and the specific classifications of concepts from 168 upper-secondary school chemistry teachers and 336 tenth-grade students were investigated by multidimensional scaling and hierarchical cluster analysis. The results showed that the 3-D solutions were appropriate for the conceptual structures of teachers and students, respectively. Next, the conceptual structure of teachers utilized as an evaluation criterion was more scientific than that of students. The conceptual structure of students with high academic achievement was more scientific than that of the low achievers. Multidimensional scaling utilized to explore students' conceptual structure of scientific concepts can provide a new and benefic form of evidence to understand the concept learning outcome of students.

This paper reports an investigation into the psychometric properties of the COR-Advantage1.5 (COR-Adv1.5) assessment tool, a criterion-referenced observation-based instrument designed to assess the developmental abilities of children from birth through kindergarten. Using data from 8534 children participating in a state-funded preschool program for disadvantaged 4-year-olds, we employed a multilevel bifactor model to show that CORAdv1.5 has some potential as an unbiased assessment tool for tracking developmental progress in pre-kindergarten children, under a bifactor model parameterization, with fewer dimensions than were proposed by the test developer, HighScope. Examination of factor adequacy indices revealed that, despite the multidimensional structure, CORAdv1.5 items overwhelmingly reflect variance due to the general factors at the child and classroom levels. Evidence for multilevel reliability, response process validity, predictive validity, longitudinal measurement invariance, and generalizability of the items are also reported. The findings of this study have important implications for the use of COR-Adv1.5.

Student engagement is a multidimensional construct influenced by complex student, classroom, and school factors. Although prior research has examined various dimensions of student engagement, few studies have adopted a multilevel approach to simultaneously incorporate observational measures of classroom practices with student and school factors. The current study examined the association between student self-reported affective and behavioral engagement (n = 26,849 students) and observed instructional and behavioral classroom management practices within 401 classrooms in 54 high schools. We employed three-level hierarchical linear modeling and included student, classroom, and school variables. Results indicated that observed teacher use of positive behavior supports was positively and significantly associated with student reports of active engagement in the same classroom. Implications for improving teachers' classroom management practices to promote student engagement are considered. [For the Grantee Submission, see ED611739.]

Multidimensionality and hierarchical data structure are common in assessment data. These design features, if not accounted for, can threaten the validity of the results and inferences generated from factor analysis, a method frequently employed to assess test dimensionality. In this article, we describe and demonstrate the application of the multilevel bifactor model to address these features in examining test dimensionality. The tool for this exposition is the Child Observation Record Advantage 1.5 (COR-Adv1.5), a child assessment instrument widely used in Head Start programs. Previous studies on this assessment tool reported highly correlated factors and did not account for the nesting of children in classrooms. Results from this study show how the flexibility of the multilevel bifactor model, together with useful model-based statistics, can be harnessed to judge the dimensionality of a test instrument and inform the interpretability of the associated factor scores.

When selecting fillers to include in a police lineup, one must consider the level of similarity between the suspect and potential fillers. In order to reduce misidentifications, an innocent suspect should not stand out. Therefore, it is important that the fillers share some degree of similarity. Importantly, increasing suspect-filler similarity too much will render the task too difficult reducing correct identifications of a guilty suspect. Determining how much similarity yields optimal identification performance is the focus of the proposed study. Extant research on lineup construction has provided somewhat mixed results. In part, this is likely due to the subjective nature of similarity, which forces researchers to define similarity in relative terms. In the current study, we manipulate suspect-filler similarity via a multidimensional scaling model constructed using objective facial measurements. In doing so, we test the "propitious heterogeneity" and the diagnostic-feature-detection hypotheses which predict an advantage of lineups with low-similarity fillers in terms of discriminability. We found that filler similarity did not affect discriminability. We discuss limitations and future directions.

This article reports on a trace-based assessment of approaches to learning used by middle school aged children who interacted with NASA Mars Mission science, technology, engineering and mathematics (STEM) games in "Whyville," an online game environment with 8 million registered young learners. The learning objectives of two games included awareness and knowledge of NASA missions, developing knowledge and skills of measurement and scaling, applying measurement for planetary comparisons in the solar system. Trace data from 1361 interactions were analysed with nonparametric multidimensional scaling methods, which permitted visual examination and statistical validation, and provided an example and proof of concept for the multidimensional scaling approach to analysis of time-based behavioural data from a game or simulation. Differences in approach to learning were found illustrating the potential value of the methodology to curriculum and game-based learning designers as well as other creators of online STEM content for pre-college youth. The theoretical framework of the method and analysis makes use of the Epistemic Network Analysis toolkit as a post hoc data exploration platform, and the discussion centres on issues of semantic interpretation of interaction end-states and the application of evidence centred design in post hoc analysis.

A cross-national comparison was conducted between U.K. and U.S. higher education students on the Test Anxiety Measure for College Students (TAM-C). The TAM-C was administer to 1,776 U.K. and U.S. higher education students online. The results of tests of measurement invariance found the TAM-C invariant across country and gender. In addition, results of a MANOVA and follow-up ANOVAs indicated U.K. higher education students were more test anxious than U.S. higher education students and females were more test anxious than males. Implications of the findings for mental health professionals who work with higher education students in the United Kingdom and the United States are discussed.

When selecting fillers to include in a police lineup, one must consider the level of similarity between the suspect and potential fillers. In order to reduce misidentifications, an innocent suspect should not stand out. Therefore, it is important that the fillers share some degree of similarity. Importantly, increasing suspect-filler similarity too much will render the task too difficult reducing correct identifications of a guilty suspect. Determining how much similarity yields optimal identification performance is the focus of the proposed study. Extant research on lineup construction has provided somewhat mixed results. In part, this is likely because similarity is often defined in relative terms due to the subjective nature of similarity. In the current study, we propose an experiment in which we manipulate suspect-filler similarity via a multidimensional scaling model constructed using objective facial measurements. In doing so, we test the "propitious heterogeneity" and the diagnostic-feature-detection hypotheses which predict an advantage of lineups with low similarity fillers in terms of discriminability.

The purpose of the study was to develop a scale measuring academic dishonesty of undergraduate students. The sample of the study constitutes nine hundred undergraduate students selected via random sampling technique. After receiving expert's opinions for the face and content validity of the scale, the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were applied. EFA favored a six factor structure viz: cheating in examination; plagiarism; outside help; prior cheating; falsification and lying about academic assignments. Moreover, the findings obtained from the CFA showed that the structure consisting of 23 items and the six factors associated to academic dishonesty scale (ADS) had adequate consistency indices. The internal consistency indices, alpha coefficient (a = 0.831) is adequate for the academic dishonesty scale (ADS). Results from the study confirmed multidimensionality and robust psychometric properties of academic dishonesty scale.

Examinations of the words we use in our daily lives have shown that certain linguistic patterns may be indicators of underlying depressogenic schemata. The Linguistic Inquiry and Word Count (LIWC) and multidimensional scaling (MDS) have been used as methods of computerized text analysis to yield insight into how people's language use may be reflective of maladaptive psychological processes, including self-focused attention, rumination, and absolutist thinking, which are all associated with the depressogenic schemata. These findings demonstrate the significance of analyzing people's language use in the mental health profession. Research in this interdisciplinary field of natural language processing, applied linguistics, and mental health not only corroborates psychological theories regarding depression but further yields implications for prevention, diagnosis, and outcome predictions of mental health depression and anxiety.

The purpose of this study was to compare the reading skills of the fourth graders who were poor readers in different text genres (story and informative text). Fifty-six fourth-grade students who were poor readers participated in this study. Reading rates, reading errors and reading prosody characteristics of the students were examined in the study. It was concluded that although reading rates of students were higher when reading the informative text, they had also produced a higher number of errors for this text genre. This finding revealed that the informative text was completed in a shorter amount of time, but more errors occurred than when reading the story text. The findings of this study make it abundantly clear that all stakeholders should continue to work diligently in organising reading instruction and reading interventions that support reading fluency among all readers and for all text genres.

The dimensionality of a set of items is important for scale development. In practice, tools that make use of eigenvalues are often used to assess dimensionality. Parallel analysis is featured here as it is becoming an increasingly popular method for assessing the number of dimensions, and computational tools have recently been made available which will likely increase its use by practitioners. The current paper argues that methods that use eigenvalues to ascertain the number of factors may perform poorly under certain conditions, particularly for increasing levels of variable complexity and/or inter-factor correlations in the latent structure. A simulation study and an example are offered to substantiate this assertion.

Our purpose is to provide an exploratory statistical representation of initial teacher education as a complex system comprised of dynamic influential elements. More precisely, we reveal what the system looks like for differently-positioned teacher education stakeholders based on our framework for gathering, statistically analyzing, and graphically representing the results of a unique exercise wherein the participants literally mapped the system as they perceived it. Through an iterative series of inter-related studies employing cluster analysis and multidimensional scaling procedures, we demonstrate how initial teacher education may be represented as a complex system comprised of interactive agents and attributes whose perceived relationships are a function of nested stakeholder-dependent simplex systems. Furthermore, we illustrate how certain propositions of complexity theory, such as boundaries, heterogeneity, multidimensionality and emergence, may be investigated and represented quantitatively.

The objective of this study is to examine the social profile of students who are enrolled in Basic General Education in Ecuador. Both a descriptive and multidimensional statistical analysis was carried out based on the data provided by the National Survey of Employment, Unemployment and Underemployment in 2015. The descriptive analysis shows the frequency and percentages of variables used in the investigation, and the multidimensional statistical analysis demonstrates the principal and more important criteria of differentiation and classification among the clusters of students who were investigated. These methods involve factorial analysis of multiple correspondences which demonstrate criteria of differentiation and a hierarchical cluster analysis to define clusters of students according to their common traits.

Many researchers and educationist remain skeptical about the effectiveness of distance learning program and have termed it as second to the conventional training method. This perception is largely due to several challenges which exist within the management of distance learning program across the country. The general aim of the study is compare the classroom management skills exhibited by distance trained teachers to that of conventional trained teachers in their field of work. Teacher classroom management was classified into two multidimensional constructs consisting of effective behavior management skills and instructional learning format skills. A quantitative design strategy was adapted for the study. Primary data was collected using a questionnaire from 500 teachers-consisting of 250 conventional and 250 distance trained teachers--and 60 head teachers selected from basic schools across 10 regions in Ghana. Head teachers assessment of teachers served as a means of triangulating results obtained. Data obtained during survey was coded and entered into a SPSS statistical software and subsequently analysed using both descriptive and inferential methods. Results from the study reveal that there were no significant differences in all nineteen (19) items used in measuring teacher classroom control mechanism. Recommendations were made based on the findings of the study.

Measuring cognitive load is important in virtual learning environments (VLE). Thus, valid and reliable measures of cognitive load are important to support instructional design in VLE. Through three studies, we investigated the validity and reliability of Leppink's Cognitive Load Scale (CLS) and developed the extraneous cognitive load (EL) dimension into three sub-scales relevant for VLE: EL instructions, EL interaction, and EL environment. We investigated the validity of the measures using the Partial Credit Model (PCM), Confirmatory Factor Analysis (CFA), and correlations with retention tests. Study 1 (n = 73) investigated the adapted version of the CLS. Study 2 describes the development and validation of the Multidimensional Cognitive Load Scale for Virtual Environments (MCLSVE), with 140 students in higher education. Study 3 tested the generalizability of the results with 121 higher education students in a more complicated VLE. The results provide initial evidence for the validity and reliability of the MCLSVE.

Despite extensive research supporting educational acceleration for students with high academic ability, some psychologists, counselors, and educators express concerns about accelerative interventions. Such concerns often hinge on uncertainty about social acceptance, even in inclusive education settings. Research on acceleration has consistently shown benefits for students with high ability; however, there is a lack of research about grade-based acceleration in inclusive schools. This study engaged two groups of students in group concept mapping processes to examine how they conceptualized beliefs about grade-based acceleration in inclusive schools. First, 26 students in inclusive classes generated beliefs about grade-based acceleration. Then they, and a group of 14 students with high ability, structured the data by sorting and rating a synthesized list of the generated beliefs. We analyzed the sorted data using multidimensional scaling and hierarchical cluster analysis. The resultant cluster maps revealed some differences and some similarities in the ways that the two groups of students conceptualized beliefs about educational acceleration. Practical implications are discussed.

Purpose: The purpose of this paper is to investigate undergraduate industrial design students' perception of sustainable design concepts and how their conceptualization evolves as a function of their attendance to a specific sustainable design studio (SDS) course. Design/methodology/approach: Two groups of students participated in the study. Students who did not attend to SDS were in the control group, whereas students who attended SDS were in the experimental group. In total, 22 concepts, which have been highlighted in literature and the SDS course, were selected as keywords. Participants were asked to provide relatedness scores of these keywords before and after they attended the course. The data were analyzed using multidimensional scaling and pathfinder (PF) networks. Findings: Results indicate that the SDS caused a change in the conceptualization of sustainable design concepts parallel to the course outcomes and the literature. Some concepts were highlighted as conveyors that guide students to conceptualize sustainable development and design. Research limitations/implications: This study is considered a case study focusing on declarative knowledge, and owing to the low number of participants, the results should be carefully interpreted. Practical implications: The findings may support design educators to enhance their courses and promote deeper debates on teaching sustainable design. Originality/value: Two specific dimensions were found from the analysis of multidimensional scaling, and several conveyor concepts were identified from PF networks. Allocating proposed dimensions and concepts into a course may have the potentials to enhance students' perception of sustainability concepts.

Purpose: The goal of this study was to establish the perceptual underpinnings of the terms that are commonly used by patients when describing the quality of their tinnitus. Method: Using a free-classification method, 15 subjects with normal hearing placed 60 different tinnitus-like sounds into similarity clusters on a grid. Multidimensional scaling, hierarchical clustering, and acoustic analyses were used to determine the acoustic underpinnings of the perceptual dimensions and perceptual similarity. Results: Multidimensional scaling revealed three different perceptual dimensions (pitch, modulation depth + spectral elements, and envelope rate). Hierarchical clustering revealed five explicit similarity clusters: tonal, steady noise, pulsatile, low-frequency fluctuating noise, and high-frequency fluctuating. Conclusions: Results are consistent with tinnitus perceptions falling into a small set of categories that can be characterized by their acoustics. As a result, there is the potential to develop different tools to assess tinnitus using a variety of different sounds.

This study aimed to examine which values predicted depressive symptoms and hopelessness in Turkey. While it was hypothesized that values emphasizing universalism, benevolence, conformity, security, tradition, spirituality, self-direction, and achievement would predict lower levels of depressive symptoms and hopelessness, those values emphasizing power, stimulation, and hedonism would predict higher levels of depressive symptoms and hopelessness. Participants were 712 university students in Turkey. The Schwartz Values Survey Revised Turkish Version, the Beck Depression Inventory, and the Beck Hopelessness Scale were administered. Spirituality was observed as a distinct value category in Turkish culture. Higher levels of universalism predicted lower levels of depressive symptoms. While higher levels of self-direction and benevolence predicted lower levels of hopelessness, higher levels of achievement predicted higher levels of hopelessness. The findings partially fit Sagiv and Schwartz's value theory regarding the relationship between mental health and the growth and deficiency aspects of values. The findings suggested having congruent values with the environment (i.e. achievement) were not uniformly associated with positive affective states.

The purpose of this study was to examine the impact of dimensionality, common-item set format, and different scale linking methods on preserving equity property with mixed-format test equating. Item response theory (IRT) true-score equating (TSE) and IRT observed-score equating (OSE) methods were used under common-item nonequivalent groups design. The equity property was evaluated based on first-order equity (FOE) and second-order equity (SOE) properties. A simulation study was conducted based on actual item parameter estimates obtained from the TIMSS 2011 8th grade mathematics assessment. The results showed that: (i) The FOE and SOE properties were best preserved under the unidimensional condition, were poorly preserved when the degree of multidimensionality was severe. (ii) The TSE and OSE results, which were provided by using a mixed-format common-item set, preserved FOE better compared to equating results, which provided only a multiple-choice common item set. (iii) Under the unidimensional and multidimensional test structure, characteristic curve methods performed significantly better than moment scale linking methods in terms of preserving FOE and SOE properties.

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This study is one of the few studies to employ multivariate analysis in investigating dimensions of Web spaces based on social tagging data. Method: This study examines the post data collected from a set of library and information science related Websites bookmarked on Delicious.com using a Web crawler. Post data consist of the URL, usernames, tags and comments assigned by users of Delicious.com. The collected tag data were analysed based on multivariate methods, such as multidimensional scaling and structural equation modelling. Analysis: Collected data were first analysed using multidimensional scaling to explore initial relationships amongst the selected Websites. Then, confirmatory factor analysis based on structural equation modelling was employed to examine the hierarchical structure of the library and information science Web space. Results: Social tag data exhibit different dimensions in the Web space of the library and information science field. In addition, social tags confirmed the hierarchical structure of the field by showing significantly stronger relationships between the sites with similar characteristics. That is, the structure of the tagging data shows similar connections to those present in the real world. Conclusions: This study suggests a new statistical approach in social tagging and Web space analysis studies. Tag information can be used to explain the hierarchical structure of a certain domain. Methodologically, this study suggests that structural equation modelling can be a compelling method to explore hierarchical structures of nodes on the Web space.

Assessments of new pedagogical practices usually rely on instructor oriented surveys and questionnaires to measure student perceptions of teaching methods; however, fixed response categories in structured questionnaires might bias results. This paper demonstrates a mixed methods approach using open and multi-dimensional scaling (MDS) for a student-oriented exploratory analysis and visualization of perceptions of teaching methods. A scientific writing and research methodology course is a required course for first year PhD students in software engineering and geo-infomatics at Wuhan University, China. PhD students attending this course came from countries whose first language is not English, and from a variety of software engineering and geo-infomatics domains. The problem therefore, was to elicit un-mediated perceptions of course assignment, reduce and generalize the resulting data for interpretation. A graphical visualization of themes emerging in student responses to two open-ended questions about an assignment provided a basis for inferring student interests and needs. In this course, an assessment of a task oriented problem-solving experience was implemented through a mixed method strategy incorporating qualitative methods, exploratory data mining techniques, and cartographic visualization. The visualization shows that participating students generally perceived the exercise as challenging, helped them understand journal requirements, and develop ways to survey texts to extract information. The results also suggested that this consensus breaks down in terms of each participant's own goals, domain, and research interests. Unstructured questions, open coding, and MDS visualization might also prove to be helpful in the process of devising and assessing other student centered pedagogies.

Adaptive learning systems need large pools of examples for practice--thousands of items that need to be organized into hundreds of knowledge components within a domain model. Domain modeling and closely related student modeling are intensively studied in research studies. However, there is a gap between research studies and practical issues faced by developers of scalable educational technologies. The aim of this paper is to bridge this gap by connecting techniques and notions used in research papers to practical problems in development. We put specific emphasis on scalability--on techniques that enable relatively cheap and fast development of adaptive learning systems. We summarize conceptual questions in domain modeling, provide an overview of approaches in the research literature, and discuss insights based on the development and analysis of a widely used system. We conclude with recommendations for both developers and researchers in the area of adaptive learning systems.

Positioning and repositioning continue to be critical topics in business strategy, and yet a review of current business research syllabi from several institutions showed that very few institutions teach the analytical underpinnings of how perceptual maps can be created using multidimensional scaling and how they should be interpreted. At the outset, the author defines multidimensional scaling and reviews various software packages that are available to create such maps, followed by an overview of a sample assignment. Then the benefits to the students are discussed, followed by extensions in graduate and in online classes as well as the impact and effectiveness of the teaching innovation.

The purpose of the present study is to conduct an external replication into the multi-dimensionality of social presence as measured by the Computer-Mediated Communication Questionnaire (Tu, 2005). Online social presence is one of the more important constructs for determining the level of interaction and effectiveness of learning in an online environment. This study is unique as it provides the opportunity to replicate previous research on the dimensionality of the instrument and offers insight as to the nature of social presence among students enrolled in higher education postgraduate online courses.

Recently, MDS has been utilized to identify and evaluate cognitive ability latent profiles in a population. However, dimension coordinates do not carry any statistical properties. To cope with statistical incompetence of MDS, we investigated the common aspects of various studies utilizing bootstrapping, and provided an R function for its implementation.

A task force was created in a small, AACSB-accredited business school to develop a more comprehensive set of standards for faculty performance. The task force relied heavily on faculty input to identify and describe key dimensions that capture effective teaching and service performance. The result is a multi-dimensional framework that will be used by faculty and administrators to communicate and assess performance expectations. The dimensions for evaluating teaching are 1) quality of instruction, 2) innovative practices, and 3) effort or time commitment. The service dimensions are 1) contribution & leadership and 2) team player. The paper describes the steps taken to develop the framework and build faculty support for its use. The experiences of the task force and the process they used to develop the framework are relevant to those who are interested in revising and clarifying the faculty evaluation process. The final version of the framework is provided in the appendix.

This study examines linking relationships among latent test scores and how these latent linking relationships relate to observed-score linkings. Equations are used to describe the effects of correlation between underlying latent dimensions and the similarity or dissimilarity of test composition on linking functions among latent test scores. These equations describing relationships among latent test scores are used to model the results obtained from a previous simulation study, which illustrated that if the two tests have parallel structure then the linking relationship between their observed scores is subpopulation invariant regardless of the correlations between the underlying latent dimensions. The equations also model the effect that the degree of correlation between the latent dimensions has on equatability as the structure departs from parallelism.

While there have been very limited studies of the educational computing literature to analyze the research trends since the early emergence of educational computing technologies, the authors argue that it is important for both researchers and educators to understand the major, historical educational computing trends in order to inform understandings of current and future eLearning trends. This study provides the findings of an analysis of 2,694 journal articles published between 1977 and 2005 in four major, international educational computing journals. It provides the platform for a subsequent analysis for the period 2006-2013 and beyond, as future educational computing research is published. The journal articles analyzed were categorized according to their research themes. Subsequently, clustering analysis, multi-dimension scale analysis, and research diversity analysis were performed on the categorized results to explore the research trends. The research literature analysis confirmed that there were identifiable evolutionary trends dating from 1977, and, importantly, the analysis highlighted that each key breakthrough in technology was accompanied by increased educational research about those technologies to inform educational practices. Importantly, two major driving forces of the historical trends identified were technologies and pedagogical approaches. The paper concludes with explanations of how these trends from 1997-2005 have shaped the current focus on Technological Pedagogical Content Knowledge (TPACK) needed for effective current and future eLearning.

This descriptive pilot study examined the cultural differences in the dimensions of self-reported anger in Indigenous and non-Indigenous (Caucasian) students aged 10-13 years in Far North Queensland, Australia. The Multidimensional School Anger Inventory-Revised (MSAI-R) (Boman, Curtis, Furlong, & Smith, 2006) was used to measure affective, cognitive and behavioural components of anger. It was found that Indigenous students had significant but small differences on the "anger experience" (affective) and "destructive expression" (behavioural) subscales. Considerations for school staff, attempting to support and connect with Indigenous students and future research are discussed.

Principals and teachers do not use large-scale assessment results because the lack of distinct and reliable subtests prevents identifying strengths and weaknesses of students and instruction, the results arrive too late to be used, and principals and teachers need assistance to use the results to improve instruction so as to improve student learning. Therefore, it is recommended that the first assessment activity should be to clearly establish that the domain to be assessed is multidimensional. Given this, the assessment schedule should be changed so that a given subject area is assessed in non-consecutive years but the number of sittings remains the same each year. Assistance should be provided to principals and teachers so as to increase their understanding of how to use large-scale assessment results. Three suggested assessment cycles are presented, each of which increases the reliability of subtests and provides principals and teachers with at least two years to make changes in instruction.

Over the years, companies have relied on On-Line Analytical Processing (OLAP) to answer complex questions relating to issues in business environments such as identifying profitability, trends, correlations, and patterns. This paper addresses the application of OLAP in education and learning. The objective of the research presented in the paper is to depict learning data with multidimensional data cube model so as to enable tools capable of answering multifarious questions regarding instruction and learning. Educational data abound in various formats and platforms. There are also interoperability standards which enable diverse applications to interact and share data. The challenge is to provide educators and teachers with interactive tools to identify the needs of individual students and to enable personalization of instruction and learning to meet the needs of the learners. Such tools will allow teachers to analyze profiles and performance history of individual students across multiple dimensions, calculate individual students' mastery of the curriculum, and recommend necessary changes to their learning plans. The ultimate goal of the research is to improve student learning by providing the ability to establish the acquired knowledge and skills of individual students through portable learning management cubes, which would maintain the profile and learning chronicle of the learners.

In this study, the authors examined the relationship between students' life satisfaction, school engagement, and confidence in the classroom. An analysis was performed of how students' life satisfaction differs according to their housing, school type, and classroom level. The multidimensional student satisfaction scale, confidence scale in the class, and school engagement scale were used to collect data from a sample of 287 high school students. In this study, Pearson correlation coefficient, path analysis, t-test, Welch test, and Tamhane's T2 test were used to analyze the data. Goodness-of-fit indices concerning the path analysis demonstrate how variables such as school engagement and confidence in the classroom predict students' life satisfaction, indicating that the model is acceptable. The model can sufficiently explain the relationship between the observed and implied variables (x2 = 209.69, df = 60, x2/df = 3.49, "p" = 0.00, RMSEA = 0.09, NFI = 0.92, NNFI = 0.93 CFI = 0.94, IFI = 0.94, RFI = 0.90, GFI = 0.88, AGFI = 0.82, and SRMR = 0.07). The results indicated that the life satisfaction of high school students decreases as the grade level increases. It was found that students who live in school dormitories have higher life satisfaction than those who reside with their families. In addition, it was found that students who study at fine arts high schools and sports high schools have higher life satisfaction than those who study at regular high schools.

Introduction: Research to date on social support for adolescents with visual impairments (that is, blindness or low vision) has been primarily carried out in developed countries, and very little is known about the social support systems that are available for such adolescents in developing countries such as those in the Arab world. In the present study, social support among adolescents with and without visual impairments was investigated in Jordan, which is considered a developing country. Methods: Study participants included 86 adolescents with visual impairments and 73 sighted adolescents. The instrument used to collect information in this study was an Arabic version of the Multidimensional Scale of Perceived Social Support (MSPSS). Results: The level of social support reported by adolescents with visual impairments was higher than that reported by sighted adolescents. In all three domains of the MSPSS (family, friends, and significant others), students with visual impairments reported receiving more social support than those without impairments. However, there was no statistically significant effect for gender. Discussion: The results of the study are not consistent with findings of previous studies, all of which were conducted in Western countries. The disparities in experiences of adolescents with visual impairments in developed and developing countries may be attributed to differences in cultural values as well as to the conceptualization of support by those involved. Implications for practitioners: Special education teachers need to be aware of the cultural backgrounds of their students and to develop and implement culturally appropriate programs for them.

Researchers have become increasingly interested in better understanding the survey data collection process in interviewer-administered surveys. However, tools for analysing paradata capturing information about field processes, also called call record data, are still not yet fully explored. This paper introduces sequence analysis as a simple tool for investigating such data with the aim of better understanding and improving survey processes. A novel approach is to use sequence analysis within interviewers, which allows the identification of unusual interviewer calling behaviours, and may provide guidance on interviewer performance. Combining the technique with clustering, optimal matching and multidimensional scaling, the method offers a way of visualising, displaying and summarising complex call record data. The method is introduced to inform survey management and survey monitoring. The method is hence informative for adaptive survey designs and will help to identify unusual behaviour and outliers and to improve survey processes. Sequence analysis is applied to call record data from the UK Understanding Society survey. The findings inform further modelling of call record data to increase efficiency in call scheduling.

Scores estimated from multidimensional item response theory (IRT) models are not necessarily comparable across dimensions. In this article, the concept of aligned dimensions is formalized in the context of Rasch models, and two methods are described--delta dimensional alignment (DDA) and logistic regression alignment (LRA)--to transform estimated item parameters so that dimensions are aligned. Both the DDA and LRA methods are applied to real and simulated data, and it is demonstrated that both methods are broadly effective for achieving aligned scales. The routine use of scale alignment methods is recommended prior to comparing scores across dimensions.