Analysis of named entity recognition and linking for tweets

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.     

Tensions in beginning teachers’ professional identity development,accompanying feelings and coping strategies

This study examined tensions encountered by 182 beginning teachers during their professional identity development, the feelings that accompanied these tensions and the ways they tried to cope with these. Professional identity tensions stem from an unbalanced personal and professional side of (becoming) a teacher. Tensions that are often mentioned by beginning teachers concerned conflicts between what they desire and what is possible in reality. Female teachers reported more tensions than their male colleagues, while final-year student teachers did not differ from first-year in-practice teachers in the number of tensions they experienced. Tensions were often accompanied by feelings of helplessness, anger or an awareness of shortcomings. Most beginning teachers tried to cope with their tensions by speaking to their significant others or by searching for a solution themselves. Insights into professional identity tensions experienced by beginning teachers are important in order to form a better understanding of the support they need.     

Child and home predictors of early numeracy skills in kindergarten

The present study investigated the influence of home numeracy experiences on early numeracy skills in kindergarten after controlling for cognitive and linguistic precursors. Eighty-nine children (mean age = 6.1 years) were tested on cognitive, linguistic, and early numeracy skills, and their parents completed a questionnaire on home numeracy practices and expectations. The results showed a unique contribution of parent–child numeracy activities and parents’ numeracy expectations on early numeracy outcomes next to individual child factors (i.e., early literacy skills and grammatical ability), stressing the importance of home numeracy experiences on early numeracy skills.     

Usable guidelines for usable websites? An analysis of five e-government heuristics

Many government organizations use web heuristics for the quality assurance of their websites. Heuristics may be used by web designers to guide the decisions about a website in development, or by web evaluators to optimize or assess the quality of an existing website. Despite their popularity, very little is known about the usefulness of heuristics for web designers and evaluators. In this study, five government heuristics are examined with special attention to their presumed use, content, validity, and presentation format. Our findings raise questions about the usability of heuristics for web design and web evaluation purposes. More research into the actual use and effects of web heuristics is needed.     

Assessing and Validating Effects of a Data‐Based Decision‐Making Intervention on Student Growth for Mathematics and Spelling

Data‐based decision making (DBDM) is presumed to improve student performance in elementary schools in all subjects. The majority of studies in which DBDM effects have been evaluated have focused on mathematics. A hierarchical multiple single‐subject design was used to measure effects of a 2‐year training, in which entire school teams learned how to implement and sustain DBDM, in 39 elementary schools. In a multilevel modeling approach, student achievement in mathematics and spelling was analyzed to broaden our understanding of the effects of DBDM interventions. Student achievement data covering the period from August 2010 to July 2014 were retrieved from schools’ student monitoring systems. Student performance on standardized tests was scored on a vertical ability scale per subject for Grades 1 to 6. To investigate intervention effects, linear mixed effect analysis was conducted. Findings revealed a positive intervention effect for both mathematics and spelling. Furthermore, low‐SES students and low‐SES schools benefitted most from the intervention for mathematics.     

Exploring the role of assessment criteria during teachers’ collaborative judgement processes of students’ portfolios

Student portfolios are increasingly used for assessing student competences in higher education, but results about the construct validity of portfolio assessment are mixed. A prerequisite for construct validity is that the portfolio assessment is based on relevant portfolio content. Assessment criteria, are often used to enhance this condition. This study aims to identify whether assessment criteria can improve content, argumentation and communication during teacher moderation while judging student portfolios. Six teachers scored 32 student portfolios in dyads with and without assessment criteria. Their judgement processes were qualitatively analysed. Results indicated that the quality of their judgement processes was low, since teachers based their judgements mainly on their own personal opinion and less on evidence found in the portfolio. Teachers barely paid attention to quality checks and easily agreed with each other. When teachers used assessment criteria, the quality of their judgements slightly improved. They based their judgements more on relevant evidence, used less personal experiences and more often checked the quality of their judgement processes. It is concluded that the quality of teacher portfolio judgement is low, and that the use of assessment criteria can enhance its quality.     

Interactions in vocational education: negotiation of meaning of students and teaching strategies

This study aimed to describe verbal student–teacher interactions in vocational education from a socio-cultural perspective on negotiation of meaning. Teaching as part of these interactions is addressed by a combination of diagnosing, checking and intervening strategies. A study was conducted in which students (n students?=?20) and teacher (n teachers?=?5) from Social Work (SW) and Information and Communication Technology (ICT) worked together in small groups (n groups?=?5) discussing vocational core problems. Each group held five discussions (n discussions?=?25). All discussions were audio recorded and transcribed before they were analysed for negotiation of meaning including teaching strategies. The results showed that 5–8% of the interactions include negotiation of meaning. Interactions in SW groups revealed more negotiation of meaning than in interactions in ICT groups. Teaching strategies mainly included checking and intervening activities in favour of diagnosing activities. Furthermore, teachers used meta-cognitive and conceptual interventions most frequently. The implications of these results are discussed by reflecting on occupational differences and on how negotiation of meaning including teaching strategies can be enhanced.     

Mixture Simultaneous Factor Analysis for Capturing Differences in Latent Variables Between Higher Level Units of Multilevel Data

Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (e.g., personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that might explain the covariances among variables; for example, the Big Five personality structure. In the case of multilevel data, one might wonder whether or not the same covariance (factor) structure holds for each so-called data block (containing data of 1 higher level unit). For instance, is the Big Five personality structure found in each country or do cross-cultural differences exist? The well-known multigroup EFA framework falls short in answering such questions, especially for numerous groups or blocks. We introduce mixture simultaneous factor analysis (MSFA), performing a mixture model clustering of data blocks, based on their factor structure. A simulation study shows excellent results with respect to parameter recovery and an empirical example is included to illustrate the value of MSFA.