Comparing and combining real and virtual experimentation: an effort to enhance students' conceptual understanding of electric circuits

Abstract  The purpose of this study was to investigate value of combining Real Experimentation (RE) with Virtual Experimentation (VE) with respect to changes in students' conceptual understanding of electric circuits. To achieve this, a pre–post comparison study design was used that involved 88 undergraduate students. The participants were randomly assigned to an experimental (45 students) and a control group (43 students). Each group attended a one semester course in physics for preservice elementary school teachers. Both groups used the same inquiry-based curriculum materials. Participants in the control group used RE to conduct the study's experiments, whereas, participants in the experimental group used RE in the first part of the curriculum and VE in another part. Conceptual tests were administered to assess students' understanding of electric circuits before, during and after the teaching intervention. Results indicated that the combination of RE and VE enhanced students' conceptual understanding more than the use of RE alone. A further analysis showed that differences between groups on that part of the curriculum in which the experimental group used VE and the control group RE, in favour of VE.     

Examining the role of computer‐supported knowledge‐building discourse in epistemic and conceptual understanding

This study characterized students' online collaborative discourse from a theory‐building perspective and examined its relation to epistemic and conceptual understanding. Fifty‐two fifth graders' Knowledge Forum discussions on electricity were analysed. Discourse moves were coded within the inquiry threads, and two key epistemic patterns were identified: problem‐centred uptake and theory‐building moves. Analysis showed that higher‐quality discourse threads included more problem‐centred uptake moves in which ideas were built more coherently on each other to address the central problem. There were also more theory‐building moves on explanation and sustain inquiry. We also examined the relationship between discourse moves and conceptual‐epistemic understanding. Regression analyses showed that problem‐centred uptake predicted epistemic cognition beyond prior epistemic cognition and that theory‐building moves on explanation predicted students' conceptual understanding beyond their prior science understanding. Implications for fostering more productive discourse and sophisticated epistemic cognitions using online discussion are discussed.     

The application of Simulation‐Assisted Learning Statistics (SALS) for correcting misconceptions and improving understanding of correlation

Simulation‐based computer assisted learning (CAL) is recommended to help students understand important statistical concepts, although the current systems are still far from ideal. Simulation‐Assisted Learning Statistics (SALS) is a simulation‐based CAL that is developed with a learning model that is based on cognitive conflict theory to correct misconceptions and enhance understanding of correlation. In this study, a mixed method (embedded experiment model) was utilized to examine the effects of SALS‐based learning compared with lecture‐based learning. The sample was composed of 72 grade‐12 students, who were randomly assigned to either the experimental group or the comparison group. The findings reveal that the SALS‐based learning approach is significantly more effective than lecture‐based learning, in terms of correcting students' misconceptions and improving their understanding of correlation. The study also uses quantitative and qualitative data to examine how the learning model of the SALS‐based learning approach contributes to the enhanced learning outcomes. Finally, practical suggestions were made with regard to directions for future studies.     

Effects of simulation‐based learning on students' statistical factual,conceptual and application knowledge

The purpose of this study was to (1) examine the effects of a storyline on learners' factual, conceptual and application knowledge with the use of a simulation for teaching introductory statistical skills and to (2) explore students' subjective enjoyment of various learning activities often used in statistics education. In order to conduct the study, two versions of a simulation were developed that differed in the presence or absence of a storyline attribute. Sixty‐four graduate students were randomly assigned to one of the two intervention conditions. Both intervention groups demonstrated significantly higher learning gains after interacting with the simulation. Particularly, both simulation‐based interventions had a positive significant effect on the acquisition of application knowledge and skills. However, no significant differences between the intervention groups on any learning outcome explored in the study were found. Results also showed that students rated the simulation used in the study as a more enjoyable learning activity in comparison to reading a textbook, lecture or teamwork. Students from the simulation without a storyline intervention reported higher enjoyment than the other intervention group. Implications of the findings for understanding the instructional benefits and shortcomings of embedding a storyline in digital learning content are discussed.     

Improving ESL writing using an online formulaic sequence word‐combination checker

Writing correct English sentences can be challenging. Furthermore, writing correct formulaic sequences can be especially difficult because accepted combinations do not follow clear rules governing which words appear together in a sequence. One solution is to provide examples of correct usage accompanied by statistical feedback from web‐based applications for learners to emulate. The goal of the study was to investigate whether such a dedicated web‐based program that provides instant examples of correct word combinations and detailed results of who used these examples, where and when, can improve students' writing. Data were collected from 74 ESL (English as a Second Language) undergraduate students divided into control and experimental groups, who performed formulaic sequence tests, wrote passages in English, followed by interviews with members of the experimental group. The results show that students who used the software outperformed those who did not, on the tests and the overall grades for their writing. Implementing formulaic sequence check programs in writing classes to help students decide which words can appear together, under which circumstances, and in which context is recommended.     

A covering Ka‐band two‐way switch filter module using a three‐line and an E‐plane waveguide band‐pass filters

A millimeter‐wave ultrawideband two‐way switch filter module is presented in this article. The switch filter module covers whole Ka‐band (26–40 GHz), and is composed of two wideband band‐pass filters and two monolithic microwave integrated circuit (MMIC) single pole two throw (SP2T) switches. One filter is realized using E‐plane iris waveguide band‐pass filter, and another is realized by a novel 11‐pole three‐line microstrip structure band‐pass filter. Compared with the traditional three‐line filter, the proposed three‐line filter not only retains virtues of the traditional three‐line filter, but also resolves drawbacks of it, which include discontinuities between adjacent sections, many parameters of design, and no effective matching circuits at input/output ports. The developed switch filter module is fabricated using hybrid integrated technology, which has a size of 51 × 26 × 9.8 mm³, and interconnections between MMICs and microstrip are established by bond wires. The fabricated switch filter module exhibits excellent performances: for two different states, the measured insertion loss and return loss are all better than 7 and 10 dB in each pass‐band, respectively. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:305–310, 2015.     

Prediction of student’s mood during an online test using formula-based and neural network-based method

Building computerized mechanisms that will accurately, immediately and continually recognize a learner’s affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence could be combined so as to optimize inferences about affective states during an online self-assessment test. A formula-based method has been developed for the prediction of students’ mood, and it was tested using data emanated from experiments made with 153 high school students from three different regions of a European country. The same set of data is analyzed developing a neural network method. Furthermore, the formula-based method is used as an input parameter selection module for the neural network method. The results vindicate to a great degree the formula-based method’s assumptions about student’s mood and indicate that neural networks and conventional algorithmic methods should not be in competition but complement each other for the development of affect recognition systems. Moreover, it becomes apparent that neural networks can provide an alternative for and improvements over tutoring systems’ affect recognition methods.     

Identifying students’ mathematical skills from a multiple-choice diagnostic test using an iterative technique to minimise false positives

There is anecdotal evidence that a significant number of students studying computing related courses at degree level have difficulty with sub-GCE mathematics. Testing of students’ skills is often performed using diagnostic tests and a number of computer-based diagnostic tests exist, which work, essentially, by testing one specific diagnostic skill at a time.