Understanding the Effects of Using Parsons Problems to Scaffold Code Writing for Students with Varying CS Self-Efficacy Levels

Introductory programming courses aim to teach students to write code independently. However, transitioning from studying worked examples to generating their own code is often difficult and frustrating for students, especially those with lower CS self-efficacy in general. Therefore, we investigated the impact of using Parsons problems as a code-writing scaffold for students with varying levels of CS self-efficacy. Parsons problems are programming tasks where students arrange mixed-up code blocks in the correct order. We conducted a between-subjects study with undergraduate students (N=89) on a topic where students have limited code-writing expertise. Students were randomly assigned to one of two conditions. Students in one condition practiced writing code without any scaffolding, while students in the other condition were provided with scaffolding in the form of an equivalent Parsons problem. We found that, for students with low CS self-efficacy levels, those who received scaffolding achieved significantly higher practice performance and in-practice problem-solving efficiency compared to those without any scaffolding. Furthermore, when given Parsons problems as scaffolding during practice, students with lower CS self-efficacy were more likely to solve them. In addition, students with higher pre-practice knowledge on the topic were more likely to effectively use the Parsons scaffolding. This study provides evidence for the benefits of using Parsons problems to scaffold students' write-code activities. It also has implications for optimizing the Parsons scaffolding experience for students, including providing personalized and adaptive Parsons problems based on the student's current problem-solving status.Comment: Peer-Reviewed, Accepted for publication in the proceedings of the 2023 ACM Koli Calling International Conference on Computing Education Researc

Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Game

While open-ended self-explanations have been shown to promote robust learning in multiple studies, they pose significant challenges to automated grading and feedback in technology-enhanced learning, due to the unconstrained nature of the students' input. Our work investigates whether recent advances in Large Language Models, and in particular ChatGPT, can address this issue. Using decimal exercises and student data from a prior study of the learning game Decimal Point, with more than 5,000 open-ended self-explanation responses, we investigate ChatGPT's capability in (1) solving the in-game exercises, (2) determining the correctness of students' answers, and (3) providing meaningful feedback to incorrect answers. Our results showed that ChatGPT can respond well to conceptual questions, but struggled with decimal place values and number line problems. In addition, it was able to accurately assess the correctness of 75% of the students' answers and generated generally high-quality feedback, similar to human instructors. We conclude with a discussion of ChatGPT's strengths and weaknesses and suggest several venues for extending its use cases in digital teaching and learning.Comment: Be accepted as a Research Paper in 18th European Conference on Technology Enhanced Learnin

How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment

As Large Language Models (LLMs) gain in popularity, it is important to understand how novice programmers use them. We present a thematic analysis of 33 learners, aged 10-17, independently learning Python through 45 code-authoring tasks using Codex, an LLM-based code generator. We explore several questions related to how learners used these code generators and provide an analysis of the properties of the written prompts and the generated code. Specifically, we explore (A) the context in which learners use Codex, (B) what learners are asking from Codex, (C) properties of their prompts in terms of relation to task description, language, and clarity, and prompt crafting patterns, (D) the correctness, complexity, and accuracy of the AI-generated code, and (E) how learners utilize AI-generated code in terms of placement, verification, and manual modifications. Furthermore, our analysis reveals four distinct coding approaches when writing code with an AI code generator: AI Single Prompt, where learners prompted Codex once to generate the entire solution to a task; AI Step-by-Step, where learners divided the problem into parts and used Codex to generate each part; Hybrid, where learners wrote some of the code themselves and used Codex to generate others; and Manual coding, where learners wrote the code themselves. The AI Single Prompt approach resulted in the highest correctness scores on code-authoring tasks, but the lowest correctness scores on subsequent code-modification tasks during training. Our results provide initial insight into how novice learners use AI code generators and the challenges and opportunities associated with integrating them into self-paced learning environments. We conclude with various signs of over-reliance and self-regulation, as well as opportunities for curriculum and tool development.Comment: 12 pages, Peer-Reviewed, Accepted for publication in the proceedings of the 2023 ACM Koli Calling International Conference on Computing Education Researc

Porphyrin Functionalized Carbon Quantum Dots for Enhanced Electrochemiluminescence and Sensitive Detection of Cu²⁺

Porphyrin (TMPyP) functionalized carbon quantum dots (CQDs-TMPyP), a novel and efficient carbon nanocomposite material, were developed as a novel luminescent material, which could be very useful for the sensitive detection of copper ions in the Cu2+ quenching luminescence of functionalized carbon quantum dots. Therefore, we constructed a sensitive “signal off” ECL biosensor for the detection of Cu2+. This sensor can sensitively respond to copper ions in the range of 10 nM to 10 μM, and the detection limit is 2.78 nM. At the same time, it has good selectivity and stability and a benign response in complex systems. With excellent properties, this proposed ECL biosensor provides an efficient and ultrasensitive method for Cu2+ detection

Characterization of the Inclusion Complexes of Isothiocyanates with γ-Cyclodextrin for Improvement of Antibacterial Activities against <i>Staphylococcus aureus</i>

The aim of this study was to develop inclusions formed by γ-cyclodextrin (γ-CD) and three isothiocyanates (ITCs), including benzyl isothiocyanate (BITC), phenethyl isothiocyanate (PEITC), and 3-methylthiopropyl isothiocyanate (MTPITC) to improve their controlled release for the inhibition of Staphylococcus aureus (S. aureus). These inclusion complexes were characterized using X-ray diffraction, Fourier-transform infrared, thermogravimetry, and scanning electron microscopy (SEM), providing appropriate evidence to confirm the formation of inclusion complexes. Preliminary evaluation of the antimicrobial activity of the different inclusion complexes, carried out in vitro by agar diffusion, showed that such activity lasted 5–7 days longer in γ-CD-BITC, in comparison with γ-CD-PEITC and γ-CD-MTPITC. The biofilm formation was less in S. aureus treated with γ-CD-BITC than that of BITC by using crystal violet quantification assay and SEM. The expression of virulence genes, including sarA, agr, cp5D, cp8F, clf, nuc, and spa, showed sustained downregulation in S. aureus treated with γ-CD-BITC for 24 h by quantitative real-time polymerase chain reaction (qRT-PCR). Moreover, the growth of S. aureus in cooked chicken breast treated with γ-CD-BITC and BITC was predicted by the Gompertz model. The lag time of γ-CD-BITC was 1.3–2.4 times longer than that of BITC, and correlation coefficient (R2) of the secondary models was 0.94–0.99, respectively. These results suggest that BITC has a more durable antibacterial effect against S. aureus after encapsulation by γ-CD

A Longitudinal Investigation of the Associations Among Parenting, Deviant Peer Affiliation, and Externalizing Behaviors: A Monozygotic Twin Differences Design

Non-shared parenting and deviant peer affiliation are linked to differences in externalizing behaviors between twins. However, few studies have examined these two non-shared environments simultaneously. The present study examined the transactional roles of differential parenting (i.e., warmth and hostility) and deviant peer affiliation on monozygotic (MZ) twin differences in externalizing behaviors using a two-wave longitudinal study of twins and their parents. The sample consisted of 520 pairs of MZ twins (46.5% males, 53.5% females), with a mean age of 13.86 years (SD = 2.10) at the T1 assessment, residing in Beijing, China. The association between non-shared hostility in parenting and adolescent externalizing behaviors was mainly explained by a child-driven effect whereby the twin with a higher level of externalizing behaviors than his or her co-twin was more likely to receive more hostility from the parents. Similarly, the relationship between deviant peer affiliation and adolescent externalizing behaviors supported the selection effect whereby the twin with a higher level of externalizing behaviors than his or her co-twin was more likely to affiliate with deviant peers. The theoretical and practical implications of these findings are discussed

DataSheet1_Development of QSRR model for hydroxamic acids using PCA-GA-BP algorithm incorporated with molecular interaction-based features.pdf

As a potent zinc chelator, hydroxamic acid has been applied in the design of inhibitors of zinc metalloenzyme, such as histone deacetylases (HDACs). A series of hydroxamic acids with HDAC inhibitory activities were subjected to the QSRR (Quantitative Structure–Retention Relationships) study. Experimental data in combination with calculated molecular descriptors were used for the development of the QSRR model. Specially, we employed PCA (principal component analysis) to accomplish dimension reduction of descriptors and utilized the principal components of compounds (16 training compounds, 4 validation compounds and 7 test compounds) to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. We performed double cross-validation approach for obtaining a more convincing model. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptor to represent the interactions between analytes and the mobile phase. Our results indicated that the incorporation of molecular interaction-based features significantly improved the accuracy of the QSRR model, (R2 value is 0.842, RMSEP value is 0.440, and MAE value is 0.573). Our study not only developed QSRR model for the prediction of the retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.</p