Comparing Multiple Theories about Learning with Physical and Virtual Representations: Conflicting or Complementary Effects?

A Radial Basis Function Neural Network Approach to Predict Preschool Teachers' Technology Acceptance Behavior

With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function (RBF) neural network (NN) modeling technique to predict preschool instructors' technology usage in classes based on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers' technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model (TAM) with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models' dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN's prediction on psychological data, opening up a new interdisciplinary field of research.

Examining Virtual Manipulatives for Teaching Computations With Fractions to Children With Mathematics Difficulty

As digital technology use increases in K-12 education, greater numbers of strategies become available to support students in mathematics. One technology that provides students diverse representations of mathematical concepts is virtual manipulatives. Although instruction featuring representations with physical manipulatives possesses a large body of research, the virtual form lacks comparable study, particularly with young children experiencing mathematics difficulty or identified with a mathematics learning disability. These students often demonstrate challenges learning integral skills such as fractions that subsequently affect their academic success in future years. This study examined the use of virtual manipulatives paired with explicit instruction and a system of least prompts for teaching computations with fractions to three elementary students with mathematics difficulty. A functional relation was found using a single-subject multiple probe design between the treatment condition and students' accuracy performance solving problems. These results and their implications for the field at-large are discussed.

Augmented Reality for Presenting Real-Time Data During Students' Laboratory Work: Comparing a Head-Mounted Display With a Separate Display

Multimedia learning theories suggest presenting associated pieces of information in spatial and temporal contiguity. New technologies like Augmented Reality allow for realizing these principles in science laboratory courses by presenting virtual real-time information during hands-on experimentation. Spatial integration can be achieved by pinning virtual representations of measurement data to corresponding real components. In the present study, an Augmented Reality-based presentation format was realized via a head-mounted display and contrasted to a separate display, which provided a well-arranged data matrix in spatial distance to the real components and was therefore expected to result in a spatial split-attention effect. Two groups of engineering students (N = 107; Augmented Reality vs. separate display) performed six experiments exploring fundamental laws of electric circuits. Cognitive load and conceptual knowledge acquisition were assessed as main outcome variables. In contrast to our hypotheses and previous findings, the Augmented Reality group did not report lower extraneous load and the separate display group showed higher learning gains. The pre- and posttest assessing conceptual knowledge were monitored by eye tracking. Results indicate that the condition affected the visual relevancy of circuit diagrams to final problem completion. The unexpected reverse effects could be traced back to emphasizing coherence formation processes regarding multiple measurements.

Smart Sensors for Augmented Electrical Experiments

With the recent increase in the use of augmented reality (AR) in educational laboratory settings, there is a need for new intelligent sensor systems capturing all aspects of the real environment. We present a smart sensor system meeting these requirements for STEM (science, technology, engineering, and mathematics) experiments in electrical circuits. The system consists of custom experiment boxes and cables combined with an application for the Microsoft HoloLens 2, which creates an AR experiment environment. The boxes combine sensors for measuring the electrical voltage and current at the integrated electrical components as well as a reconstruction of the currently constructed electrical circuit and the position of the sensor box on a table. Combing these data, the AR application visualizes the measurement data spatially and temporally coherent to the real experiment boxes, thus fulfilling demands derived from traditional multimedia learning theory. Following an evaluation of the accuracy and precision of the presented sensors, the usability of the system was evaluated with n=20 pupils in a German high school. In this evaluation, the usability of the system was rated with a system usability score of 94 out of 100.

Teaching Chemistry with Arduino Experiments in a Mixed Virtual-Physical Learning Environment

A study with K-9 Greek students was conducted in order to evaluate how the declarative knowledge acquisition was affected by incorporating Arduino experiments in secondary Chemistry Education. A Digital Application (DA) that blends the use of the Arduino sensors' experiments with digital educational material, including Virtual Labs (VLs), was constructed from scratch to be used through the Interactive Board (IB) as a learning tool by three different student groups (N = 154). In the first stage of the learning process, all groups used only the digital material of the DA. In the second stage, the three groups used different learning tools of the DA. Through the IB, the first group used Arduino experiments, the second one the VLs, and the third only static visualizations. A pre- to post-test statistical analysis demonstrated that the first two groups were equivalent in regard to achievement in declarative knowledge tests and of a higher level than the third group. Therefore, it can be concluded that conducting Arduino experiments in a mixed virtual-physical environment results in equivalent learning gains in declarative knowledge as those attained by using VL experimentation through the IB.