The mathematics teacher identity of two early career mathematics teachers and the influence of their working communities on its development

Journal of Mathematics Teacher Education - To better understand the critical early years of teaching and teacher identity development, this study explored the mathematics teacher identity of two...     

Secondary School Students’ Understanding of Science and Their Socioscientific Reasoning

Research in socioscientific issue (SSI)-based interventions is relatively new (Sadler in Journal of Research in Science Teaching 41:513–536, 2004; Zeidler et al. in Journal of Research in Science Teaching 46:74–101, 2009), and there is a need for understanding more about the effects of SSI-based learning environments (Sadler in Journal of Research in Science Teaching 41:513–536, 2004). Lee and Witz (International Journal of Science Education 31:931–960, 2009) highlighted the need for detailed case studies that would focus on how students respond to teachers’ practices of teaching SSI. This study presents case studies that investigated the development of secondary school students’ science understanding and their socioscientific reasoning within SSI-based learning environments. A multiple case study with embedded units of analysis was implemented for this research because of the contextual differences for each case. The findings of the study revealed that students’ understanding of science, including scientific method, social and cultural influences on science, and scientific bias, was strongly influenced by their experiences in SSI-based learning environments. Furthermore, multidimensional SSI-based science classes resulted in students having multiple reasoning modes, such as ethical and economic reasoning, compared to data-driven SSI-based science classes. In addition to portraying how participants presented complexity, perspectives, inquiry, and skepticism as aspects of socioscientific reasoning (Sadler et al. in Research in Science Education 37:371–391, 2007), this study proposes the inclusion of three additional aspects for the socioscientific reasoning theoretical construct: (1) identification of social domains affecting the SSI, (2) using cost and benefit analysis for evaluation of claims, and (3) understanding that SSIs and scientific studies around them are context-bound.     

The Effect of the Most-Attractive-Distractor Location on Multiple-Choice Item Difficulty

Abstract

The arrangement of response options in multiple-choice (MC) items, especially the location of the most attractive distractor, is considered critical in constructing high-quality MC items. In the current study, a sample of 496 undergraduate students taking an educational assessment course was given three test forms consisting of the same items but the positions of the most attractive distractor varied across the forms. Using a multiple-indicators–multiple-causes (MIMIC) approach, the effects of the most attractive distractor's positions on item difficulty were investigated. The results indicated that the relative placement of the most attractive distractor and the distance between the most attractive distractor and the keyed option affected students’ response behaviors. Moreover, low-achieving students were more susceptible to response-position changes than high-achieving students.     

An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash

In this study, an artificial neural network （ANN） model for studying the strength properties of steel fiber reinforced concrete （SFRC） containing fly ash was devised. The mixtures were prepared with 0 wt%, 15 wt%, and 30 wt% of fly ash, at 0 vol.%, 0.5 vol.%, 1.0 vol.% and 1.5 vol.% of fiber, respectively. After being cured under the standard conditions for 7, 28, 90 and 365 d, the specimens of each mixture were tested to determine the corresponding compressive and flexural strengths. The pa- rameters such as the amounts of cement, fly ash replacement, sand, gravel, steel fiber, and the age of samples were selected as input variables, while the compressive and flexural strengths of the concrete were chosen as the output variables. The back propagation learning algorithm with three different variants, namely the Levenberg-Marquardt （LM）, scaled conjugate gradient （SCG） and Fletcher-Powell conjugate gradient （CGF） algorithms were used in the network so that the best approach can be found. The results obtained from the model and the experiments were compared, and it was found that the suitable algorithm is the LM algorithm. Furthermore, the analysis of variance （ANOVA） method was used to determine how importantly the experimental parameters affect the strength of these mixtures.     

Modeling and real-time fault identification in transformers

In this paper, a different internal fault modeling and an identification algorithm are presented. There has been an increasing concern about turn-to-turn faults in transformers because of the high costs of unexpected outages. It is not always possible to analyze the transformer behavior under such faults at rated conditions, since the tests are highly destructive. To develop transformer internal fault detection technique, a transformer model to simulate internal faults is required. This paper describes a novel technique and methodology for modeling and identifying transformer internal faults by using transmission line method (TLM) and fuzzy reasoning technique based on dynamic principal component analysis (PCA), respectively. The transformer has been modeled considering non-linearities as hysteresis and saturation. Transformer internal fault currents are successfully discriminated from the rated currents. The degree and priority of transformer internal faults are obtained by the proposed method. It is suited for implementation on computers because of no computation complexity. Hence, the proposed algorithm can be used effectively in real-time fault identification problems.     

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

This article presents a novel tuning design of Proportional-Integral-Derivative (PID) controller in the Automatic Voltage Regulator (AVR) system by using Cuckoo Search (CS) algorithm with a new time domain performance criterion. This performance criterion was chosen to minimize the maximum overshoot, rise time, settling time and steady state error of the terminal voltage. In order to compare CS with other evolutionary algorithms, the proposed objective function was used in Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for PID design of the AVR system. The performance of the proposed CS based PID controller was compared to the PID controllers tuned by the different evolutionary algorithms using various objective functions proposed in the literature. Dynamic response and a frequency response of the proposed CS based PID controller were examined in detail. Moreover, the disturbance rejection and robustness performance of the tuned controller against parametric uncertainties were obtained, separately. Energy consumptions of the proposed PID controller and the PID controllers tuned by the PSO and ABC algorithms were analyzed thoroughly. Extensive simulation results demonstrate that the CS based PID controller has better control performance in comparison with other PID controllers tuned by the PSO and ABC algorithms. Furthermore, the proposed objective function remarkably improves the PID tuning optimization technique.     

Some reflections on learning from early school experience

This paper is a report of a study on early school experience as part of a Teacher Education programme in Turkey. The programme requires second-year students to act as students only, observing their mentors in classrooms rather than practising teaching so that they can gain a wider view of the teaching profession. The study analyses the experiences of these students, therefore, particularly focusing on the extent to which this scheme is conducive to making students learn from their experience. The findings from the qualitative data suggest that careful attention must be paid to major factors shaping this process, such as the school atmosphere as a whole, the nature of tasks for observation, the quality of interaction between student-mentor, and, more importantly, the opportunities for students to develop an inquiry-based approach to teaching.     

THE ROLE OF CATTELL–HORN–CARROLL (CHC) COGNITIVE ABILITIES IN PREDICTING WRITING ACHIEVEMENT DURING THE SCHOOL‐AGE YEARS

Writing is a complex academic task—it involves numerous mental processes. Given the necessity for developing writing skills from elementary to secondary school, this study aimed to investigate the role of broad cognitive abilities derived from the Cattell–Horn–Carroll (CHC) theory of intelligence in predicting skills associated with writing achievement. The normative sample from the fourth edition of the Woodcock–Johnson Tests of Cognitive Abilities and the Woodcock–Johnson Tests Academic Achievement were used to examine the relationships between broad CHC abilities and academic achievement in writing. The findings of this study suggest that the broad CHC abilities Comprehension‐Knowledge, Processing Speed, and Fluid Reasoning are especially important predictors of basic writing skills and written expression during the school‐age years. In general, changes in the strength of the association between cognitive abilities and academic achievement in writing are observed over time, as the cognitive demands involved in the writing increase in complexity in later grades.     

Item and testlet position effects in computer-based alternate assessments for students with disabilities

Item positions in educational assessments are often randomized across students to prevent cheating. However, if altering item positions results in any significant impact on students’ performance, it may threaten the validity of test scores. Two widely used approaches for detecting position effects – logistic regression and hierarchical generalized linear modelling – are often inconvenient for researchers and practitioners due to some technical and practical limitations. Therefore, this study introduced a structural equation modeling (SEM) approach for examining item and testlet position effects. The SEM approach was demonstrated using data from a computer-based alternate assessment designed for students with cognitive disabilities from three grade bands (3–5, 6–8, and high school). Item and testlet position effects were investigated in the field-test (FT) items that were received by each student at different positions. Results indicated that the difficulty of some FT items in grade bands 3–5 and 6–8 differed depending on the positions of the items on the test. Also, the overall difficulty of the field-test task in grade bands 6–8 increased as students responded to the field-test task in later positions. The SEM approach provides a flexible method for examining different types of position effects.     

The Effects of Item Format and Cognitive Domain on Students’ Science Performance in TIMSS 2011

Research in Science Education - The purpose of this study was to examine eighth-grade students’ science performance in terms of two test design components, item format, and cognitive domain....