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.     

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.     

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.     

Content and timing of feedback in a web-based learning environment: effects on learning as a function of prior knowledge

This study investigated the effectiveness of different types of feedback content (elaborate versus global) and feedback timing (immediate versus delayed) for learning genetics in a web-based learning environment as a function of learners' prior knowledge. It was hypothesized that learning outcomes of students with low prior knowledge would be fostered by immediate elaborate feedback, whereas those of students with more prior knowledge would be enhanced by delayed global feedback. Students' perceptions of the feedback they received were explored. Results showed a significant positive effect of global feedback on learning outcomes for higher prior knowledge learners, although those who received elaborate feedback gave a higher appreciation rating. The findings are discussed in terms of implications for the design and delivery of feedback in web-based learning environments.     

The Effect of Passive and Active Education Methods Applied in Repetition Activities on the Retention of Anatomical Knowledge

This study examines the long-term retention of anatomical knowledge from 180 students after various repetition activities. The retention of anatomical knowledge was assessed by multiple-choice tests at five different points in time: before and after a course in Functional Anatomy, before and after repetition activities that occurred 14 weeks after this course, and 28 weeks after this course to establish long-term retention. Students were divided into five groups: one without any repetition activity, one with a restricted repetition activity (the multiple-choice test), and three groups that were offered repetition activities (traditional lecture, e-learning module, and small group work in the dissection room). During all three repetition activities the same information was conveyed, and this content was not revisited in other courses for the duration of the study. The results showed that students who did not engage in a repetition activity scored significantly lower on the long-term retention test compared to all other groups (ANCOVA: P = 0.0001). Pair-wise comparison with estimated means showed that the other four groups, regardless of the type of repeating activity, did not differ in the amount of knowledge they retained during any of the five assessments (P = 0.008, P = 0.0001, P = 0.001, and P = 0.0001, respectively). This study suggests that the type of repetition activity has no effect on knowledge retention both immediately following the activity and in the long term. It is concluded that the repetition of anatomical knowledge in any form is beneficial for students and will likely improve student outcomes in a curriculum that builds on prior knowledge.     

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.     

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.