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Based on the study of the feature extraction algorithm based on the multiple empirical mode decomposition of the Duffing equation, this paper proposes a corresponding improved algorithm, completes the identification and analysis of the psychological pressure dimension space under the audiovisual induction method, and designs two typical psychological types of music and pictures. Based on the stress induction experiment, an audiovisual-induced psychological stress recognition system based on EEG (electroencephalogram) signals was built. Aiming at the problem that the spatial uniform sampling method cannot well reflect the dynamic characteristics of the multivariate EEG signal, based on the Duffing equation, a nonuniform sampling algorithm that adaptively selects the projection direction is proposed. At present, the use of the Duffing equation to detect weak unknown signals is to select a set of fixed parameters. Analysis of these two aspects to determine the parameters of the system is based on the parameter analysis of the Duffing equation oscillator. Due to the sensitivity of the Duffing equation to the initial value, the choice of parameters has a great influence on the detection effect. In response to this situation, the relationship between the parameters and initial values of the Duffing equation is analyzed. From the relationship between the parameters and the initial values, the influence of different parameters on the detection effect is analyzed to verify the superiority of the current equation parameters. First, the multichannel EEG signal is nonuniformly sampled multiempirical modal decomposition, and an effective intrinsic modal function is selected to extract the mental stress EEG characteristics. Experimental results show that the EEG signal recognition algorithm based on the Duffing equation effectively extracts EEG signal features and improves the classification accuracy of mental stress EEG signals.

In this study, we demonstrate the tunable modulation of the electromagnetically induced transparency (EIT) effect using L-shaped complementary graphene metamaterials. The structure consists of a horizontal line slot and a vertical line slot, representing the bright and dark modes, respectively. The synergistic interaction between the bright and dark modes generates a pronounced transparent window within the transmission spectrum. Owing to symmetry, the EIF effect can be realized in two perpendicular polarization directions. Compared to metallic metamaterials, graphene metamaterials are tunable by controlling the Fermi energy of graphene via the gate voltage rather than by redesigning the structure. The regulation of the Fermi energy level in the complementary graphene metamaterials presented herein is more straightforward than that in the discrete graphene configurations. By tuning the EIT transparent window, we enabled actively controlled sensing capabilities and the realization of slow light effects. This work illuminates potential applications in the development of environmental sensors, slow-light devices, and terahertz modulators.

The purpose of this study is to examine how students may hold coexisting and competing conceptual ideas and how students’ uses of such ideas may evolve during the process of conceptual change by combining two complementary methods including model analysis and the conceptual framework model of knowledge integration. Conceptual change is fundamental to science learning, highlighting the processes of students’ transitions from predominantly relying on alternative views to using scientific ones. Recent studies suggest a growing consensus that alternative views and scientific conceptions coexist and compete in students’ learning. However, explicit research on how the states of coexistence and competition evolve during the process of conceptual change is underdeveloped. Building on existing research, the model analysis provides a quantitative framework to represent the different consistent and coexisting/competing states through probabilities of consistent and/or mixed (coexisting) use of incorrect and scientific ideas. Meanwhile, the conceptual framework model of knowledge integration provides content-specific, cognitive explanations for the developmental levels and processes of conceptual change from novice to expert. By combining these two methods, researchers can gain insights into how the coexistence and competition among different conceptual ideas may evolve as conceptual change progresses. To target the research goal, a model-based knowledge integration assessment of Newton’s third law is developed to measure students’ levels of knowledge integration and model states in a single test. Quantitative data were collected from Chinese high school students, supplemented by interviews with a subgroup from the same population. The model analysis results show the heterogeneity of conceptual development and the evolving competition across novice, intermediate, and expert students, as well as across different concept dimensions. The interviews provide cognitive explanations for the observed model states, framed from the lens of knowledge integration. These insights can inform the development of effective assessments and instructional strategies to facilitate favorable conceptual change in student learning.

The galvanic cell is a typical interdisciplinary topic, which is primarily taught in chemistry but its underpinning is closely related to physics. Student learning in galvanic cell has been extensively studied in chemistry education, which has revealed a large number of misconceptions that are difficult to change through traditional instruction. A source of the learning difficulties is that most students lack an explanatory framework to integrate the many complex phenomena and processes into a coherent knowledge system. As a result, students often rely on memorization of terms, laws, and equations in solving problems but without a meaningful understanding of their reasoning. To address the deficit in learning, this study aims to help students develop an explanatory framework for supporting an integrated knowledge structure. Specifically, a conceptual framework model for galvanic cell is developed based on the central idea from physics, which is used as the core concept for mechanistic explanations of the related chemistry concepts. Guided by the conceptual framework, an instrument is developed to assess the knowledge integration in student learning of galvanic cell. A combination of qualitative and quantitative measures was used to assess the levels of knowledge integration in learning galvanic cell with a large number of Chinese high school and college students. The assessment results show that the conceptual framework model can effectively represent the knowledge structures of students at different levels of knowledge integration. The galvanic cell conceptual framework and assessment results can provide useful resources for teachers to develop instruction for promoting knowledge integration.

Student learning in simple electric circuits has been an important area in physics education research. This study builds off a previous investigation that applied the conceptual framework model to examine knowledge integration in student learning of simple electric circuits and developed a multiple-choice concept test for assessing knowledge integration in simple electric circuits. In this study, a conceptual-framework-based teaching intervention was developed and implemented in a controlled study with high school students in China to evaluate the effectiveness of the new instruction. Using the instrument developed in the previous study, a pretest, a post-test, and a delayed post-test were conducted with both groups of students. The delayed post-test was included to further evaluate knowledge retention as evidence of knowledge integration. The results suggest that the conceptual-framework-based teaching intervention was effective in promoting knowledge integration compared to the existing instruction.

Misconceptions coexisting with scientific understanding pose significant challenges in physics education. Inhibitory control may enable individuals to overcome interference from misconceptions. However, discerning the role of inhibitory control becomes intricate when the saliency of scientific- and misconception-related features varies in a problem. This study investigates the role of inhibitory control in overcoming such misconceptions when scientific and misconception-related features are similarly salient, particularly focusing on the range-time (range determines flight time) misconception. We adopted a negative priming (NP) paradigm with the eye-tracking technique in the study. Thirty-six college physics majors participated in the experiment. In the NP paradigm, the participants first compared the flight times in a neutral (same range) or incongruent (longer range, less flight time) primes and then completed the same task in a congruent (longer range, more flight time) probe. Results revealed longer reaction times for incongruent primes compared to neutral primes and longer reaction times to respond to congruent probes following incongruent primes than when neutral primes were present beforehand, suggesting that the inhibitory control may be involved in overcoming the interference from the range-time misconception. Moreover, the eye-tracking analysis highlighted an early overt attention to the salient misconception-related feature (longer range) during primes, along with additional attentional investment necessary to inhibit the range-time misconception. During probes, an early shift of attention away from the salient misconception-related feature and a greater investment of attention on processing the congruent probes after the incongruent primes than that after the neutral primes were observed, indicating a possible effect of the previous inhibitory control in the primes on the subsequent cognitive process on the probes. These findings contribute to a deeper understanding of the cognitive process underlying physics conceptual change and provide insights into physics education.

Abstract Gold(I)‐thiolate complexes have served as primary building blocks for diverse Au nanostructure synthesis strategies. A delicate approach to characterize and control Au(I)‐thiolate motif formation and assembly on the surface is needed as it can potentially solve challenges associated with utilizing gold nanomaterials in many applications. Here, the controllable generation of flower‐shaped surface gold nanostructures (FSGNs) is demonstrated by manipulating the formation‐assembly process of Au(I)‐dodecanethiolate motifs within nanoscale surface droplets. The morphology and structure of the resulting Au nanostructures are governed by internal convection flows and interfacial energy, modulated by the nanodroplet composition and substrate wettability. The obtained FSGNs are proven to act as versatile scaffolds for the selective generation of Au spiky nanostars. These FSGNs can also be utilized to functionalize nanodroplet‐based reactors, boosting the fluorescent intensity of Nile red (NR) fluorophores and decomposing NR via catalytic reaction. Remarkably, with FSGN functionalized droplets smaller than a radius of 500 nm, the decomposition rate of NR can reach ≈0.01 s−1. These results highlight a miniaturized, controllable, and automated method for the in situ production of 3D gold nanostructures on substrates, offering prospects for fast surface nanostructure fabrication and efficient environmental pollutant treatment.

The gas-phase Cn (n = 6–31) carbocyclic clusters were systematically investigated based on DFT using ABCluster software. In this work, two geometrical parameters, degree of circularity and the ratio of carbon atoms occupying the fitted circle, were defined for the geometrical circle formed by Cn (n = 6–31) carbocyclic by curve-fitting the coordinates of the carbon atoms, and it is found that even-numbered carbon rings are always more rounded than odd-numbered ones. The study of relative stability of Cn ring clusters, on the one hand, revealed that even carbon rings are always more stable than odd ones. On the other hand, it proved that cyclic shape carbon clusters are always more stable than cage-like structure ones. In addition, the density of states, simulated electron absorption spectra, polarization response to electrostatic field, π-electron population, and topological properties of bond critical points were compressively studied. This work effectively filled the lack of research on odd-numbered carbocycles, a member of the carbocyclic family, and showed readers the physical and chemical reasons for the relative deformation of odd-numbered carbocycles.

Abstract Thin film transistors (TFTs) are key components for the fabrication of electronic and optoelectronic devices, resulting in a push for the wider exploration of semiconducting materials and cost‐effective synthesis processes. In this report, a simple approach is proposed to achieve 2‐nm‐thick indium oxide nanosheets from liquid metal surfaces by employing a squeeze printing technique and thermal annealing at 250 °C in air. The resulting materials exhibit a high degree of transparency (>99 %) and an excellent electron mobility of ≈96 cm2 V−1 s−1, surpassing that of pristine printed 2D In2O3 and many other reported 2D semiconductors. UV‐detectors based on annealed 2D In2O3 also benefit from this process step, with the photoresponsivity reaching 5.2 × 104 and 9.4 × 103 A W−1 at the wavelengths of 285 and 365 nm, respectively. These values are an order of magnitude higher than for as‐synthesized 2D In2O3. Utilizing transmission electron microscopy with in situ annealing, it is demonstrated that the improvement in device performances is due to nanostructural changes within the oxide layers during annealing process. This work highlights a facile and ambient air compatible method for fabricating high‐quality semiconducting oxides, which will find application in emerging transparent electronics and optoelectronics.

By employing a tiny frequency offset across the array elements, a frequency diverse array (FDA) produces a beampattern with the property of being range-angle-dependent. However, the beampattern of a traditional FDA has many maxima at undesired range-angle couples, which is unfavorable for target localization and jamming suppression. In this paper, a novel FDA scheme with Hamming interelement spacing, namely, the HIS-FDA, is proposed. By adopting Hamming window weighted interelement spacing and frequency offsets optimized by the genetic algorithm, the HIS-FDA produces a dot-shaped well-performed beampattern, which has superior performance over the existing FDA systems, especially in the angle dimension. The multiple input multiple output technique and multiple matched filters are employed at the receiver to solve the time-varying problem. A new multicarrier (MC) technique termed the sinusoidal MC approach is also presented to improve the beampattern performance, especially in the range dimension. Our proposed scheme outperforms the existing FDA systems in terms of the mainlobe width in the range and angle profiles, the sidelobe level, and the range-angle spatial region of the mainlobe. Comparison results are provided to validate the superiority of the proposed scheme.

In this study, a conceptual framework of measurement uncertainty was developed and used to guide the development of a multiple-choice concept test for the assessment of students’ knowledge integration in learning measurement uncertainty. Based on assessment data and interview results, students were identified into three levels of knowledge integration including novice, intermediate, and expertlike. The reasoning pathways of students at different levels revealed a progression of reasoning from a rudimentary surface level to a deep understanding that can be mapped in the conceptual framework. This work demonstrates the possibility of identifying a quantitative categorization scheme to model knowledge integration as well as its utility in teaching and learning. Overall, the assessments and interviews revealed common and persistent difficulties in students’ understanding of measurement uncertainty. In addition, students at different levels of knowledge integration demonstrate unique types of knowledge states that can be represented in the conceptual framework, making it a useful tool for analyzing different reasoning pathways and knowledge structures.

Aiming at the phenomenon of partial discharge caused by the insulation fault of switchgears, an ultra-high frequency (UHF) partial discharge online monitoring system based on the Internet of Thing (IoT) is designed. The hardware of the system mainly consists of UHF sensors, signal conditioning circuits, and data acquisition circuits to realize the monitoring of the discharge signal. The monitoring data is uploaded to the cloud platform through the 4G DTU module, and the host computer software based on B/S architecture is designed on the server side, which can monitor the operation status of the switchgear anytime and anywhere. The experimental results show that the detection system is stable and reliable and can meet practical needs.

Achieving knowledge integration for deep learning requires students to develop well-connected knowledge structures through the central idea of a concept. However, a number of studies on student learning in momentum have revealed persistent difficulties in understanding its fundamental concept, particularly in relating net force, time, and change in momentum with each other. This study evaluates the effectiveness of a teaching intervention based on the conceptual framework of momentum that targets knowledge integration. The evaluation outcomes of the intervention provide encouraging evidence suggesting that the conceptual framework model can be a valuable guide to the development of instruction for promoting knowledge integration and deep learning.

Work and mechanical energy is a fundamental topic in introductory physics. Studies in existing literature have shown that students have difficulties in understanding work and mechanical energy, particularly the topic of work-energy theorem. To study students’ knowledge integration in learning work and mechanical energy, a conceptual framework model of work and mechanical energy was developed and applied to guide the design of an assessment for measuring students’ level of knowledge integration. Using the assessment, qualitative and quantitative data were collected in two high schools in an eastern Chinese city. The results reveal that the conceptual framework model can effectively represent the students’ knowledge structures at different levels of knowledge integration. In addition, the assessment is shown effective in identifying unique features of knowledge integration, including context dependence and fragmentation of knowledge components, memorization-based problem-solving strategies, and lack of meaningful connections between work and change in kinetic energy. The conceptual framework of work and mechanical energy and assessment results can provide useful information to facilitate instructional designs to promote knowledge integration.

Students hold a variety of initial (mis)conceptions that are inconsistent with scientific knowledge and hinder their physics learning. The initial (mis)conceptions could coexist with the scientific ones, even after a conceptual change. Inhibitory control may help overcome initial (mis)conceptions. This study investigated if and how inhibitory control can overcome position-velocity indiscrimination (PVI), a common kinematics misconception. We designed a negative priming paradigm with various prime-probe item pairs. College physics majors had to judge if the items describing the instant that two locomotives have the same velocity were correct. When congruent probes (same position-same velocity) were followed by incongruent primes (same position-different velocity), participants performed worse than when neutral primes (i.e., not passing points) were presented beforehand. The result verified a typical negative priming effect, indicating that inhibitory control is involved in overcoming the PVI misconception. Congruent probes preceded by incongruent primes implicitly triggering the PVI misperception had a lower negative priming effect than explicitly activating it. The results indicated that inhibitory control was needed to overcome the PVI misconception, and the explicit activation of the misconception required more inhibitory control.

Student learning in simple electric circuits has been extensively studied, which has revealed a large number of persistent misunderstandings. This study applies the conceptual framework model to investigate student difficulties in the knowledge integration perspective. The results are used to guide the design of a concept test that targets the different stages of knowledge integration in student learning of electric circuits. Specifically, two areas of research have been conducted. First, based on content analysis by experts and a review of the literature on students’ conceptual understandings, a conceptual framework for electric circuits is developed. The conceptual framework model is then applied to guide the development of a multiple-choice concept test for assessment of knowledge integration in learning electric circuits. Both qualitative and quantitative data were collected from high school students who had completed the learning of electric circuits. The results confirmed that the conceptual framework model can effectively represent the knowledge structures of students at different levels of knowledge integration. In addition, the assessment outcomes also reveal that the concept test is effective in identifying unique features of knowledge integration, including context dependence and fragmentation of knowledge components, memorization-based problem solving, difficulty in transfer to novel contexts, and lack of meaningful connections between microscopic and macroscopic models of electric current. The assessment outcomes can also provide practical information for instruction to promote knowledge integration in learning electric circuits.

To effectively test the detection performance of the monopulse radar under cooperative jamming of dual stealth aircraft formation, a blinking jamming model based on the interval of time-varying dual-aircraft formation is established for the first time in this study. By geometric modeling of the blinking jamming mode of the dual-aircraft formation, the authors solved the equivalent time-varying dynamic radar cross section of the penetration operation of the dual-aircraft formation; then, by taking the signal-to-interference ratio of the aircraft fuselage target echo in the actual formation as a parametric variable, we derived the equations for instantaneous detection distance, instantaneous detection probability, and cumulative detection probability of the monopulse radar in the formation projectile, and these equations were applicable to calculation under the actual jamming mode of dual-aircraft formation. Through simulation, the authors concluded for the first time that as the supersonic endurance speed increased, releasing jamming could reduce the radar detection probability considerably by more than 60%, while releasing projectiles could increase the formation’s risk of being detected by the radar by 30.37%. Thus, to effectively reduce the stability of the radar’s detection and tracking performance, the jamming power of the dual stealth aircraft formation should be no smaller than 0.5 kW and the signal gain of the formation should be no less than 20 dB.

Abilities in scientific thinking and reasoning have been emphasized as core areas of initiatives, such as the Next Generation Science Standards or the College Board Standards for College Success in Science, which focus on the skills the future will demand of today’s students. Although there is rich literature on studies of how these abilities develop in students across grade levels, the research community has not reached consensus on their definition, modeling, or assessment. To advance research in this important area, a coherent theoretical model of scientific reasoning is needed for practically guiding instruction and assessment. For decades, the only instrument available for large-scale application was the Lawson’s Classroom Test of Scientific Reasoning, but the instrument has demonstrated validity weaknesses and ceiling limitations, and its design is missing an explicit modeling framework for justifying the included skills. As a result, there is an urgent need for the development of a comprehensive modeling framework of scientific reasoning and a valid scientific reasoning assessment that targets the wide-ranging skills required for 21st century learners. This paper reports on the development of a modeling framework of scientific reasoning along with a new assessment instrument, adding to the research literature in a much needed area. The modeling framework integrates research in scientific and causal reasoning and operationally defines the skills and subskills that underlie the reasoning for knowledge development through scientific inquiry. Subsequently, this framework is used to guide the development of an assessment instrument on scientific reasoning. The validity and reliability of the instrument, which have been established based on large-scale testing, will also be discussed.

Newton’s third law is one of the most important concepts learned early in introductory mechanics courses; however, ample studies have documented a wide range of students’ misconceptions and fragmented understandings of this concept that are difficult to change through traditional instruction. This research develops a conceptual framework model to investigate students’ understanding of Newton’s third law through the knowledge integration perspective. The conceptual framework is established with a central idea emphasizing forces as quantitative measures of physical interactions instead of using the common action-reaction language. Guided by the conceptual framework, assessment and interview results reveal that students’ concepts of Newton’s third law are fragmented without deep understanding. Specifically, three main issues within students’ understanding have been identified: (i) students have a disconnect between time order of events and causal reasoning, (ii) students rely on a memorized equal-and-opposite rule to identify interaction forces, and (iii) students directly link action-reaction language to the belief in a causal relation between the interaction forces. The framework is then applied to develop a new instruction intervention that explicitly targets the central idea of Newton’s third law. Results from pre- and post-testing show that the intervention is effective in helping students develop more integrated understandings of Newton’s third law. Overall, this study shows the potential benefits of applying the conceptual framework method to model student knowledge structures and guide assessment and instruction for promoting knowledge integration and deep learning.

A deep understanding of mechanical waves is crucial for students to succeed in studying many advanced physics topics. Studies in existing literature have revealed that students often have widespread difficulties and misconceptions on wave propagation. This research develops and applies a conceptual framework model to examine students’ understanding of mechanical wave propagation from the knowledge integration perspective. Based on the existing literature and interview results, the conceptual framework model of wave propagation was developed and used to guide the development of a multiple-choice test that targets the assessment of knowledge integration in students’ understanding of wave propagation. The assessment was given to first-year college students enrolled in a calculus-based introductory physics course at a medium-ranking Chinese university. The results suggested that after traditional instruction the majority of the students only achieved intermediate level knowledge integration with fragmented knowledge structures and lacked a deep conceptual understanding of wave propagation. The conceptual framework and assessment outcomes were then used to inform the development of new instruction, which made explicit emphasis on teaching the central idea of the conceptual framework and making connections to the central idea within students’ knowledge structures. The effectiveness of the instruction was evaluated with the same pool of college students separated into an experimental group and a control group. Pre-post testing results suggested that the new instruction significantly improved students’ performance on questions that require an integrated knowledge structure, indicating advancement in knowledge integration and deep understanding.

Causal reasoning is a fundamental cognitive process that supports a wide range of learning activities such as making predictions and inferences, explaining phenomena, and drawing conclusions. In physics learning, causal reasoning plays an important role in developing conceptual understanding and problem-solving skills. This study builds off the existing work to investigate aspects of students’ causal reasoning in understanding Newton’s third law. Quantitative assessment is conducted to explicitly probe four attributes of causal reasoning including the causal relation between a pair of interaction forces, magnitudes of interaction forces, the time order of interaction forces, and the action-reaction language. Interviews are also conducted to identify details of students’ reasoning pathways. The subjects of this study include high school and college students from China. The results suggest that after instruction on Newton’s third law many students still hold a belief of causal relation between the pair of interaction forces. Further analysis on students’ response patterns and interviews suggests that students’ belief in causality is strongly associated with asymmetries in scenario contexts due to dominant features, the scenario timeline, and the action-reaction language. In addition, the results also demonstrate a progression of four developmental levels of causal reasoning that each reveals a set of unique thinking pathways for using the asymmetries in reasoning to determine causality. The findings of this study suggest that the lack of correct causal reasoning may cause students to develop fragmented conceptual understanding, which should be addressed in future research and instruction. Detailed analysis of the findings and educational implications on assessment and instruction are discussed.

Problem-solving categorization tasks have been well studied and used as an effective tool for assessment of student knowledge structure. In this study, a traditional free-response categorization test has been modified into a multiple-choice format, and the effectiveness of this new assessment is evaluated. Through randomized testing with Chinese college students, the multiple-choice categorization test has been shown to provide equivalent measurement compared to the open-ended test. In addition, the influence of including diagrams in problems on students’ categorization performances has also been studied, which suggests that the inclusion of diagrams in problems can improve students’ performances in identifying the relevant concepts. The results also show that the knowledge structures of the Chinese college students are in the early transitional stage between novices and experts.

Concept inventories (CIs) are commonly used in pre-post instruction to study student conceptual change. For consistency in assessment interpretation, a CI’s assessment construct is desired to maintain invariance across different test times. In this study, the longitudinal measurement invariance (LMI) analysis under the confirmatory factor analysis framework was used to examine the stability of the factor structure between pretest and post-test of two commonly used CIs, i.e., the Force Concept Inventory (FCI) and Conceptual Survey of Electricity and Magnetism (CSEM). A number of existing and modified models were examined in this paper. The results confirmed that all factor models of the FCI fitted well with both pre- and post-test data. For CSEM, acceptable fits were obtained with a reduced version of the CI. When reliability analysis was performed for the factors of these models, most modified models were found to be more reliable than the existing models. The modified models were further tested in LMI analysis, in which a sequence of models with increasingly restrictive parameter constraints was examined. For the FCI, LMI analysis demonstrated the existence of partial strict invariance, i.e., common factor structures, factor loadings, and item thresholds, and equally observed residual variances for all the items except items 2 and 29. For the CSEM, after excluding 10 items, a reduced version was found to hold the strict invariance criteria. These findings reveal that changes in scores of the whole FCI and the reduced CSEM can be attributed to changes in the latent constructs measured by the CIs, which confirms these two CIs as reliable instruments to study students’ conceptual change over time in introductory physics courses.

Momentum is a foundational concept in physics. Although it is often taught in introductory mechanics courses, there are a limited number of studies on this topic in the literature. The results from these studies have consistently shown that students have difficulties in understanding momentum, especially the connections between net force, time, and change in momentum, which are signs of fragmented knowledge structure and poor knowledge integration. This study adopts the conceptual framework representation to model student understanding and guide the design of an assessment test on momentum. Informed by the previous work on knowledge integration, the conceptual framework maps out the key concepts and their connections within a student’s knowledge structure. Recent studies have shown that a conceptual framework can be used as a guide to create assessment items that follow different reasoning pathways, probing various misconceptions and student difficulties. In this study, an assessment of momentum was developed and tested among a large number of U.S. college freshmen and Chinese high school students. Based on testing and interview results, students’ understanding is separated into three progression levels of knowledge integration including novicelike, transitional, and expertlike. Furthermore, the comparison between the two countries’ curriculum and momentum test results indicates that an emphasis on the central idea of impulse-momentum theorem can be an essential instructional strategy to help students make the necessary connections within their knowledge structure, leading to a deeper conceptual understanding of momentum.

In physics education research (PER), concept inventories (CIs) have become standard instruments for assessing students’ learning throughout instruction. To promote widespread use of concept inventories, previous studies have developed an approach to split a full length CI into short versions of CIs. This research extends the existing method to fully utilize the item response theory framework in equating and linking between the short CIs and the full length CIs. Three quantitative studies have been conducted: First, the extended algorithm is applied to divide the Brief Electricity and Magnetism Assessment (BEMA) into two half-length BEMAs (HBEMAs). Through a series of test-equating and validation analysis, the HBEMAs are confirmed to measure the same latent constructs of student understanding of electricity and magnetism to that of the original BEMA at a similar level of reliability. The second study establishes equivalent score conversions among the three versions of BEMA using the Stocking-Lord method, which has the best performance on equating error reduction among several methods explored. It is also confirmed that the equivalent statistical characteristics of the three versions of BEMA are equity and population invariant. In the third study, the extended algorithm is applied to link and compare the BEMA and the Conceptual Survey of Electricity and Magnetism (CSEM). After linking the BEMA and CSEM assessment scales, it becomes possible to directly convert and compare students’ performances on the two CIs. It is found that the scales of BEMA and CSEM are almost identical after scale transformation. Based on these studies, it can be suggested that all short and long versions of BEMA and CSEM can be used interchangeably after scale transformation.

Light interference is an essential topic for understanding the wavelike nature of light, however, there are limited studies on modeling and assessing students’ misconceptions and learning difficulties in this area. Based on the knowledge integration modeling approach, a conceptual framework for light interference is developed and used to model student understanding and guide the development of an assessment tool on light interference. The conceptual framework provides a representation of students’ reasoning pathways to clearly show their connections through different conceptual components and contextual features of problem-solving settings. This type of representation focuses on showing students’ knowledge structures regarding the features of integration and fragmentation. Experts’ reasoning pathways always flow through a central idea of a concept with well-established connections to a wide range of contextual features and conditions. These connections form an integrated knowledge structure, which demonstrates deep understanding. In contrast, novices often focus on surface details without linking the central idea, forming fragmented local connections that link directly between contextual features and task outcomes. As a result, novice students’ problem solving often relies on memorization of formula and solutions without any deep understanding. Through testing and interviews at a large Chinese university, a light interference test (LIT) has been developed and validated. Assessment results also demonstrate that students with a strong conceptual understanding of the central idea are able to apply expertlike reasoning to familiar and novel questions regardless of the contextual details. Meanwhile, students with weaker or nonexistent understanding of the central idea often struggle when novel situations are presented. LIT provides a useful tool to measure students’ conceptual understanding on light interference and probe thought pathways of students’ reasoning that can further indicate students’ knowledge structure and levels of deep understanding.

Information definitions across many disciplines commonly treat information as a physical world entity. Information measures are used along with other physical variables undistinguished for modeling physical systems. Building on previous work, this research explicitly defines information as a unique category of entity that is created by intelligent agents to represent aspects of the physical world but that is not part of the physical world. This leads to the formation of the dual-space information modeling (DSIM) framework, which clearly distinguishes an information space from the physically based material space. The separation of information and material spaces allows new insight and flexibility into modeling complex systems. In this research, DSIM based agent models are applied to study the impact of information asymmetry to marketing behaviors. This paper demonstrates the effectiveness of the DSIM framework in the modeling process and how emergent behavior from these systems is encapsulated in the dual-space.

In science, technology, engineering, and mathematics education there has been increased emphasis on teaching goals that include not only the learning of content knowledge but also the development of scientific reasoning skills. The Lawson classroom test of scientific reasoning (LCTSR) is a popular assessment instrument for scientific reasoning. Through large scale applications, however, several issues have been observed regarding the validity of the LCTSR. This paper will review the literature on the assessment of scientific reasoning and provide a detailed analysis of the current version of LCTSR in regards to its validity and measurement features. The results suggest that the LCTSR is a practical tool for assessing a unidimensional scale of scientific reasoning. The instrument has a good overall reliability for the whole test. However, inspections of individual question pairs reveal a variety of validity concerns for five of the total twelve question pairs. These five question pairs have a considerable amount of inconsistent response patterns. Qualitative analysis also indicates wide ranging question design issues. Therefore, assessment of subskills involved with the five question pairs may have less power due to their questionable validity.

Assessment instruments composed of two-tier multiple choice (TTMC) items are widely used in science education as an effective method to evaluate students’ sophisticated understanding. In practice, however, there are often concerns regarding the common scoring methods of TTMC items, which include pair scoring and individual scoring schemes. The pair-scoring method is effective in suppressing “false positives” at the cost of missing possible middle states of progression of student understanding. On the other hand, the individual scoring method captures an undistinguished middle level but is prone to rewarding guessing, which leads to “false positives”. In addition, this middle level does not discriminate the progression between knowing the result and explaining the reason, which limits the capacity of drawing meaningful implications from the assessment outcomes. To address the concerns with the current scoring methods, it is valuable to explore new scoring method(s) that can fully utilize the information measured with TTMC items. In this study, a number of scoring models are studied using Rasch analysis on data of a popular TTMC test, the Lawson classroom test of scientific reasoning (LCTSR), collected from four considerably different populations. The results show that the model fit quality of the scoring methods varies with student population and item design. In general, there is no one-fits-all solution; however, given the new information obtained in this study, a three-step process is suggested that can guide the development of new mixed scoring models tailored for a particular population and or test. The evaluation results show that the mixed models produce the most reliable model fitting and better than average goodness of fit. Furthermore, the results in this study also confirm previous studies, which suggest that it is harder to come up with a correct explanation than to just know the answer.

In a recent study, the 30-question Force Concept Inventory (FCI) was theoretically split into two 14-question “half-length” tests (HFCIs) covering the same set of concepts and producing mean scores that can be equated to those of the original FCI. The HFCIs require less administration time and reduce test-retest issues when different versions are used in pre-post testing. This study experimentally evaluates the practical validity and measurement uncertainty of the HFCIs with three different college student populations. Measured mean scores on each HFCI were within ∼3% of each other at every university. Measured mean score differences between the HFCI and FCI were also within ∼3%. These differences are less than the value of a single question on the 30-question FCI and are not statistically significant. The overall results suggest that, in conditions similar to this study, the HFCIs can be used as alternatives to the full-length FCI when total scores or score gains are the measurement goals.

It is well documented that when solving problems experts first search for underlying concepts while students tend to look for equations and previously worked examples. The overwhelming majority of end-of-chapter (EOC) problems in most introductory physics textbooks contain only material and examples discussed in a single chapter, rarely requiring a solver to conduct a general search for underlying concepts. Hypothesizing that complete reliance on EOC problems trains students to rely on a nonexpert approach, we designed and implemented “synthesis” problems, each combining two major concepts that are broadly separated in the teaching timeline. To provide students with guided conceptual scaffolding, we encapsulated each synthesis problem into a sequence with two preceding conceptually based multiple-choice questions. Each question contained one of the major concepts covered in the subsequent synthesis problem. Results from a small-scale interview study and two large-scale written tests showed that the scaffolding encouraged students to search for and apply appropriate fundamental principles in solving synthesis problems, and that repeated training using scaffolded synthesis problems also helped students to make cross-topic transfers.

Pre-testing and post-testing is a commonly used method in Physics Education Research to assess student learning gains. It is well recognized in the community that timings and incentives in delivering conceptual tests can impact test results. However, it is difficult to control these variables across different studies. As a common practice, a pre-test is often administered either at or near the beginning of a course, while a post-test can be given either at or near the end of a course. Also, in conducting such tests there often is no norm as to whether incentives should be offered to students. Because these variations can significantly affect test results, it is important to study and document their impact. We analyzed five years of data that were collected at The Ohio State University from over 2100 students, who took both the pre-test and post-test of the Conceptual Survey of Electricity and Magnetism under various timings and incentives. We observed that the actual time frame for giving a test has a marked effect on the test results and that incentive granting also has a significant influence on test outcomes. These results suggest that one should carefully monitor and document the conditions under which tests are administered.

We present mathematical learning models—predictions of student’s knowledge vs amount of instruction—that are based on assumptions motivated by various theories of learning: tabula rasa, constructivist, and tutoring. These models predict the improvement (on the post-test) as a function of the pretest score due to intervening instruction and also depend on the type of instruction. We introduce a connectedness model whose connectedness parameter measures the degree to which the rate of learning is proportional to prior knowledge. Over a wide range of pretest scores on standard tests of introductory physics concepts, it fits high-quality data nearly within error. We suggest that data from MIT have low connectedness (indicating memory-based learning) because the test used the same context and representation as the instruction and that more connected data from the University of Minnesota resulted from instruction in a different representation from the test.

The Force Concept Inventory (FCI) is a 30-question multiple-choice assessment that has been a building block for much of the physics education research done today. In practice, there are often concerns regarding the length of the test and possible test-retest effects. Since many studies in the literature use the mean score of the FCI as the primary variable, it would be useful then to have different shorter tests that can produce FCI-equivalent scores while providing the benefits of being quicker to administer and overcoming the test-retest effects. In this study, we divide the 1995 version of the FCI into two half-length tests; each contains a different subset of the original FCI questions. The two new tests are shorter, still cover the same set of concepts, and produce mean scores equivalent to those of the FCI. Using a large quantitative data set collected at a large midwestern university, we statistically compare the assessment features of the two half-length tests and the full-length FCI. The results show that the mean error of equivalent scores between any two of the three tests is within 3%. Scores from all tests are well correlated. Based on the analysis, it appears that the two half-length tests can be a viable option for score based assessment that need to administer tests quickly or need to measure short-term gains where using identical pre- and post-test questions is a concern.

Decades of education research have shown that students can simultaneously possess alternate knowledge frameworks and that the development and use of such knowledge are context dependent. As a result of extensive qualitative research, standardized multiple-choice tests such as Force Concept Inventory and Force-Motion Concept Evaluation tests provide instructors tools to probe their students’ conceptual knowledge of physics. However, many existing quantitative analysis methods often focus on a binary question of whether a student answers a question correctly or not. This greatly limits the capacity of using the standardized multiple-choice tests in assessing students’ alternative knowledge. In addition, the context dependence issue, which suggests that a student may apply the correct knowledge in some situations and revert to use alternative types of knowledge in others, is often treated as random noise in current analyses. In this paper, we present a model analysis, which applies qualitative research to establish a quantitative representation framework. With this method, students’ alternative knowledge and the probabilities for students to use such knowledge in a range of equivalent contexts can be quantitatively assessed. This provides a way to analyze research-based multiple choice questions, which can generate much richer information than what is available from score-based analysis.

Decades of education research have shown that students can simultaneously possess alternate knowledge frameworks and that the development and use of such knowledge are context dependent. As a result of extensive qualitative research, standardized multiple-choice tests such as Force Concept Inventory and Force-Motion Concept Evaluation tests provide instructors tools to probe their students’ conceptual knowledge of physics. However, many existing quantitative analysis methods often focus on a binary question of whether a student answers a question correctly or not. This greatly limits the capacity of using the standardized multiple-choice tests in assessing students’ alternative knowledge. In addition, the context dependence issue, which suggests that a student may apply the correct knowledge in some situations and revert to use alternative types of knowledge in others, is often treated as random noise in current analyses. In this paper, we present a model analysis, which applies qualitative research to establish a quantitative representation framework. With this method, students’ alternative knowledge and the probabilities for students to use such knowledge in a range of equivalent contexts can be quantitatively assessed. This provides a way to analyze research-based multiple choice questions, which can generate much richer information than what is available from score-based analysis.