Promoting learning transfer in science through a complexity approach and computational modeling.

This article concerns the synergy between science learning, understanding complexity, and computational thinking (CT), and their impact on near and far learning transfer. The potential relationship between computer-based model construction and knowledge transfer has yet to be explored. We studied middle school students who modeled systemic phenomena using the Much.Matter.in.Motion (MMM) platform. A distinct innovation of this work is the complexity-based visual epistemic structure underpinning the Much.Matter.in.Motion (MMM) platform, which guided students' modeling of complex systems. This epistemic structure suggests that a complex system can be described and modeled by defining entities and assigning them (1) properties, (2) actions, and (3) interactions with each other and with their environment. In this study, we investigated students' conceptual understanding of science, systems understanding, and CT. We also explored whether the complexity-based structure is transferable across different domains. The study employs a quasi-experimental, pretest-intervention-posttest-control comparison-group design, with 26 seventh-grade students in an experimental group, and 24 in a comparison group. Findings reveal that students who constructed computational models significantly improved their science conceptual knowledge, systems understanding, and CT. They also showed relatively high degrees of transfer-both near and far-with a medium effect size for the far transfer of learning. For the far-transfer items, their explanations included entities' properties and interactions at the micro level. Finally, we found that learning CT and learning how to think complexly contribute independently to learning transfer, and that conceptual understanding in science impacts transfer only through the micro-level behaviors of entities in the system. A central theoretical contribution of this work is to offer a method for promoting far transfer. This method suggests using visual epistemic scaffolds of the general thinking processes we would like to support, as shown in the complexity-based structure on the MMM interface, and incorporating these visual structures into the core problem-solving activities. Supplementary Information: The online version contains supplementary material available at 10.1007/s11251-023-09624-w.

Perception of sonified representations of complex systems by people who are blind.

This research focused on examining the sonification properties that can lead people who are blind to distinguish and to identify different sounds. This research included 10 participants, all of whom were examined individually. They listened to a sonified scenario, which was generated by an agent-based NetLogo computer model of a gas particle in a container. The participants identified the different sounds as opposed to examining their ability to identify the value of the sounds or to understand the scientific phenomena as a result of hearing the model scenario. This research found that, in regard to complexity levels, the participants were able to identify stimuli that included up to four sounds. The analyses reveal that in the second trial the participants displayed heightened ability. The long-term practical benefits of this research may well influence program developers in education and rehabilitation for people who are blind. A learning environment based on sonified feedback can address a central need among people who are blind, providing equal access to learning environments equivalent to those available to sighted users and allowing independent interaction with exploratory materials and control of the learning process.

Glycemic control in adolescents with type 1 diabetes: Are computerized simulations effective learning tools?

OBJECTIVE: Type 1 diabetes mellitus (T1DM) in adolescent patients is often characterized by poor glycemic control. This study aimed at exploring the contribution of learning with computerized simulations to support: (a) mechanistic understanding of the biochemical processes related to diabetes; (b) diabetes self-management knowledge; and (c) glycemic control. We hypothesized that learning with such simulations might support adolescents in gaining a better understanding of the biochemical processes related to glucose regulation, and consequently improve their glycemic control. METHODS: A prospective case-control study was conducted in 12- to 18-year-old adolescents with T1DM (n = 85) who were routinely treated at an outpatient diabetes clinic. While the control group (n = 45) received the routine face-to-face follow-up, the intervention group (n = 40) learned in addition with computerized simulations that were embedded in pedagogically supportive activities. Participants in both groups completed a set of questionnaires regarding sociodemographic characteristics, diabetes mechanistic reasoning and diabetes self-management. Clinical data and serum glycated hemoglobin (HbA1c) levels were gathered from medical records. All the data was collected at recruitment and 3 months later. RESULTS: Analysis revealed improvement HbA1c levels in the intervention group (8.7% ± 1.7%) vs the controls (9.6% ± 1.6%) after 3 months (P < .05). Regression analysis showed that levels of diabetes mechanistic understanding and diabetes self-management knowledge, in addition to sociodemographic parameters, accounted for 31% of the HbA1c variance (P < .001). CONCLUSION: These results suggest that learning with computerized simulations about biochemical processes can improve adolescents' adherence to medical recommendations and result in improved glycemic control. Implementing scientific learning into the hospital educational setting is discussed.

Adherence to diabetes care: Knowledge of biochemical processes has a high impact on glycaemic control among adolescents with type 1 diabetes.

AIM: To evaluate the impact of patients' understanding of biochemical processes involved in glucose regulation (causal-biochemical knowledge) and of diabetes self-management knowledge on adherence to treatment recommendations among adolescents with type 1 diabetes mellitus. DESIGN: A cross-sectional study. METHODS: Adolescents with type 1 diabetes mellitus, aged 12-18 years and able to read and write in Hebrew or in Arabic were eligible. Participants were recruited between August 2016 - January 2018 during routine visits to the Paediatric Diabetes Clinic; informed consent was obtained as customary. Patients completed sociodemographic, clinical and type 1 diabetes mellitus self-management and biochemical knowledge questionnaires. Adherence to treatment was assessed by patients' serum HbA1c levels, collected from medical records. RESULTS: Ninety-seven patients participated in the study. Mean HbA1c levels were 9.2% (1.9%) and only 24 (24.7%) patients met the recommended HbA1c ≤ 7.5%. Lower HbA1c levels were strongly associated with higher family income, older age at diagnosis and with better type 1 diabetes mellitus self-management and causal-biochemical knowledge. A regression model showed that causal-biochemical knowledge contributed to the variance in HbA1c levels. Furthermore, causal-biochemical knowledge, but not self-management knowledge, was found to mediate the negative relationship between low family income and high HbA1c levels. CONCLUSIONS: Causal-biochemical knowledge is a valuable component for the adherence to diabetes care and glycaemic control. IMPACT: Our study suggests that causal knowledge is a valuable component that should be included in nursing and healthcare educational programmes for adolescents with type 1 diabetes mellitus.

Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study.

BACKGROUND: Pharmacology is a crucial component of medications administration in nursing, yet nursing students generally find it difficult and self-rate their pharmacology skills as low. OBJECTIVES: To evaluate nursing students learning pharmacology with the Pharmacology Inter-Leaved Learning-Cells environment, a novel approach to modeling biochemical interactions using a multiscale, computer-based model with a complexity perspective based on a small set of entities and simple rules. This environment represents molecules, organelles and cells to enhance the understanding of cellular processes, and combines these cells at a higher scale to obtain whole-body interactions. PARTICIPANTS: Sophomore nursing students who learned the pharmacology of diabetes mellitus with the Pharmacology Inter-Leaved Learning-Cells environment (experimental group; n=94) or via a lecture-based curriculum (comparison group; n=54). METHODS: A quasi-experimental pre- and post-test design was conducted. The Pharmacology-Diabetes-Mellitus questionnaire and the course's final exam were used to evaluate students' knowledge of the pharmacology of diabetes mellitus. RESULTS: Conceptual learning was significantly higher for the experimental than for the comparison group for the course final exam scores (unpaired t=-3.8, p<0.001) and for the Pharmacology-Diabetes-Mellitus questionnaire (U=942, p<0.001). The largest effect size for the Pharmacology-Diabetes-Mellitus questionnaire was for the medication action subscale. Analysis of complex-systems component reasoning revealed a significant difference for micro-macro transitions between the levels (F(1, 82)=6.9, p<0.05). CONCLUSIONS: Learning with complexity-based computerized models is highly effective and enhances the understanding of moving between micro and macro levels of the biochemical phenomena, this is then related to better understanding of medication actions. Moreover, the Pharmacology Inter-Leaved Learning-Cells approach provides a more general reasoning scheme for biochemical processes, which enhances pharmacology learning beyond the specific topic learned. The present study implies that deeper understanding of pharmacology will support nursing students' clinical decisions and empower their proficiency in medications administration.