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Educational Data Mining in Computer Science Education (CSEDM) is an interdisciplinary research community that combines discipline-based computing education research (CER) with educational data-mining (EDM) to advance knowledge in ways that go beyond what either research community could do on its own. The JEDM Special Issue on CSEDM received a total of 12 submissions. Each submission was reviewed by at least three reviewers, who brought expertise from both the EDM and CER communities, as well as one of special issue editors. Ultimately, three papers were accepted, for an acceptance rate of 25%. These three papers cover a variety of important topics in CSEDM research. Edwards et al. discuss the challenges of collecting, sharing and analyzing programming data, and contribute two high-quality CS datasets. Gitinabard et al. contribute new approaches for analyzing data from pairs of students working on programs together, and show how such data can inform classroom instruction. Finally, Zhang et al. contribute a novel model for predicting students' programming performance based on their past performance. Together, these papers showcase the complexities of data, analytics and modeling in the domain of CS, and contribute to our understanding of how students learn in CS classrooms.

Manganese-based contrast agents (MnCAs) have emerged as suitable alternatives to gadolinium-based contrast agents (GdCAs). However, due to their kinetic lability and laborious synthetic procedures, only a few MnCAs have found clinical MRI application. In this work, we have employed a highly innovative single-pot template synthetic strategy to develop a MnCA, MnL , and studied the most important physicochemical properties in vitro. MnL displays optimized r relaxivities at both medium (20 and 64 MHz) and high magnetic fields (300 and 400 MHz) and an enhanced r =21.1 mM s (20 MHz, 298 K, pH 7.4) upon binding to BSA (K =4.2×10 M ). In vivo studies show that MnL is cleared intact into the bladder through renal excretion and has a prolonged blood half-life compared to the commercial GdCA Magnevist. MnL shows great promise as a novel MRI contrast agent. Me Me b −1 −1 3 −1 Me Me 1 1 a

Smart theranostics are dynamic platforms that integrate multiple functions, including at least imaging, therapy, and responsiveness, in a single agent. This review showcases a variety of responsive theranostic agents developed specifically for magnetic resonance imaging (MRI), due to the privileged position this non-invasive, non-ionising imaging modality continues to hold within the clinical imaging field. Different MRI smart theranostic designs have been devised in the search for more efficient cancer therapy, and improved diagnostic efficiency, through the increase of the local concentration of therapeutic effectors and MRI signal intensity in pathological tissues. This review explores novel small-molecule and nanosized MRI theranostic agents for cancer that exhibit responsiveness to endogenous (change in pH, redox environment, or enzymes) or exogenous (temperature, ultrasound, or light) stimuli. The challenges and obstacles in the design and in vivo application of responsive theranostics are also discussed to guide future research in this interdisciplinary field towards more controllable, efficient, and diagnostically relevant smart theranostics agents.

Providing students with adaptive feedback on their progress on programming problems has been shown to motivate students and improve their performance, but little is known about how such feedback might impact student self-regulated learning during programming. Self-regulated learning (SRL) involves student planning a task, monitoring their progress, and reflecting on the outcome. We explored students’ SRL behaviors, particularly progress monitoring, when programming using each of three different scaffolds. The first scaffold is a subgoal checklist for the given programming task, the second adds automated, binary completion feedback on each subgoal, and the third adaptively reflects an automated percent progress estimate of student progress on each. Through interviews and programming logs from 17 students solving a problem in a block-based programming environment, we investigated the extent to which each scaffold supported student SRL. Our qualitative study results suggest that all three scaffolds could be useful for student SRL, but students felt that a combination of the checklist and progress feedback provided them with a balance of autonomy and motivation to persevere in programming. Furthermore, our results suggest that explaining how the automated feedback system works may have encouraged students to reason about the feedback they receive, which was a key intended outcome to improve SRL during programming.

One approach to teaching game design to students with a wide variety of disciplinary backgrounds is through team game projects that span multiple weeks, up to an entire term. However, open-ended, creative projects introduce a gamut of challenges to novice programmers. Our goal is to assist game design students with the planning stage of their projects. This paper describes our data collection process through three course interventions and student interviews, and subsequent analysis in which we learned students had difficulty expressing their creative vision and connecting the game mechanics to the intended player experience. We present these results as a step towards the goal of scaffolding the planning process for student game projects, supporting more creative ideas, clearer communication among team members, and a stronger understanding of human-centered design in software development.

Students often struggle during programming homework and may need help getting started or localizing errors. One promising and scalable solution is to provide automated programming hints, generated from prior student data, which suggest how a student can edit their code to get closer to a solution, but little work has explored how to design these hints for large-scale, real-world classroom settings, or evaluated such designs. In this paper, we present CodeChecker, a system which generates hints automatically using student data, and incorporates them into an existing CS1 online homework environment, used by over 1000 students per semester. We present insights from survey and interview data, about student and instructor perceptions of the system. Our results highlight affordances and limitations of automated hints, and suggest how specific design choices may have impacted their effectiveness.

Open-ended programming increases students' motivation by allowing them to solve authentic problems and connect programming to their own interests. However, such open-ended projects are also challenging, as they often encourage students to explore new programming features and attempt tasks that they have not learned before. Code examples are effective learning materials for students and are well-suited to supporting open-ended programming. However, there is little work to understand how novices learn with examples during open-ended programming, and few real-world deployments of such tools. In this paper, we explore novices' learning barriers when interacting with code examples during open-ended programming. We deployed Example Helper, a tool that offers galleries of code examples to search and use, with 44 novice students in an introductory programming classroom, working on an open-ended project in Snap. We found three high-level barriers that novices encountered when using examples: decision, search, and integration barriers. We discuss how these barriers arise and design opportunities to address them.

Understanding students' misconceptions is important for effective teaching and assessment. However, discovering such misconceptions manually can be time-consuming and laborious. Automated misconception discovery can address these challenges by highlighting patterns in student data, which domain experts can then inspect to identify misconceptions. In this work, we present a novel method for the semi-automated discovery of problem-specific misconceptions from students' program code in computing courses, using a state-of-the-art code classification model. We trained the model on a block-based programming dataset and used the learned embedding to cluster incorrect student submissions. We found these clusters correspond to specific misconceptions about the problem and would not have been easily discovered with existing approaches. We also discuss potential applications of our approach and how these misconceptions inform domain-specific insights into students' learning processes., 7 pages, 3 figures, Accepted in LAK'21

Project-based learning can encourage and motivate students to learn through exploring their own interests, but introduces special challenges for novice programmers. Recent research has shown that novice students perceive themselves to be "bad at programming, especially when they do not know how to start writing a program, or need to create a plan before getting started. In this paper, we present PlanIT, a guided planning tool integrated with the Snap! programming environment designed to help novices plan and program their open-ended projects. Within PlanIT, students can add a description for their project, use a to do list to help break down the steps of implementation, plan important elements of their program including actors, variables, and events, and view related example projects. We report findings from a pilot study of high school students using PlanIT, showing that students who used the tool learned to make more specific and actionable plans. Results from student interviews show they appreciate the guidance that PlanIT provides, as well as the affordances it offers to more quickly create program elements.

Instructors have limited time and resources to help struggling students, and these resources should be directed to the students who most need them. To address this, researchers have constructed models that can predict students' final course performance early in a semester. However, many predictive models are limited to static and generic student features (e.g. demographics, GPA), rather than computing-specific evidence that assesses a student's progress in class. Many programming environments now capture complete time-stamped records of students' actions during programming. In this work, we leverage this rich, fine-grained log data to build a model to predict student course outcomes. From the log data, we extract patterns of behaviors that are predictive of students' success using an approach called differential sequence mining. We evaluate our approach on a dataset from 106 students in a block-based, introductory programming course. The patterns extracted from our approach can predict final programming performance with 79% accuracy using only the first programming assignment, outperforming two baseline methods. In addition, we show that the patterns are interpretable and correspond to concrete, effective -- and ineffective -- novice programming behaviors. We also discuss these patterns and their implications for classroom instruction.

Manganese‐based contrast agents (MnCAs) have emerged as suitable alternatives to gadolinium‐based contrast agents (GdCAs). However, due to their kinetic lability and laborious synthetic procedures, only a few MnCAs have found clinical MRI application. In this work, we have employed a highly innovative single‐pot template synthetic strategy to develop a MnCA, MnLMe, and studied the most important physicochemical properties in vitro. MnLMe displays optimized r 1 relaxivities at both medium (20 and 64 MHz) and high magnetic fields (300 and 400 MHz) and an enhanced r 1 b=21.1 mM−1 s−1 (20 MHz, 298 K, pH 7.4) upon binding to BSA (K a=4.2×103 M−1). In vivo studies show that MnLMe is cleared intact into the bladder through renal excretion and has a prolonged blood half‐life compared to the commercial GdCA Magnevist. MnLMe shows great promise as a novel MRI contrast agent., We present a single‐pot template synthesis strategy for a manganese‐based MRI contrast agent, MnLMe. MnLMe is highly inert toward zinc‐transmetallation and displays enhanced T 1 relaxivity upon non‐covalent interaction with serum albumin. In vivo studies show higher contrast enhancement in the liver and longer blood half‐life than that of the commercially available contrast agent Magnevist. MnLMe shows great potential for use as a blood pool agent.

The chemistry of gallium has played a key role in nuclear medicine for half a century. Its applications have centered around two principal radionuclides, gallium-67 and gallium-68. Developments in the chemistry of gallium and its radionuclides in the last few years have led to advances in simple, convenient preparation of biomolecular conjugate-based radionuclides using novel chelating agents, including clinical applications; use of radionuclide imaging to elucidate the pharmacokinetics of gallium-based anticancer drugs; exploiting the biological chemistry of gallium as a basis for radionuclide imaging of cancer and microbial infection; potential use of gallium-67 as an Auger electron-emitting therapeutic radionuclide; and exploitation of the affinity of gallium for fluoride to develop simple methods for labeling biomolecules with the positron-emitting radionuclide fluorine-18. This review provides an update on each of these areas of gallium chemistry in the context of its interface with nuclear medicine, illustrated by recent collaborative contributions of the authors, who form a group of UK academic groups sharing common interests.

Programming environments such as Snap, Scratch, and Processing engage learners by allowing them to create programming artifacts such as apps and games, with visual and interactive output. Learning programming with such a media-focused context has been shown to increase retention and success rate. However, assessing these visual, interactive projects requires time and laborious manual effort, and it is therefore difficult to offer automated or real-time feedback to students as they work. In this paper, we introduce SnapCheck, a dynamic testing framework for Snap that enables instructors to author test cases with Condition-Action templates. The goal of SnapCheck is to allow instructors or researchers to author property-based test cases that can automatically assess students' interactive programs with high accuracy. Our evaluation of SnapCheck on 162 code snapshots from a Pong game assignment in an introductory programming course shows that our automated testing framework achieves at least 98% accuracy over all rubric items, showing potentials to use SnapCheck for auto-grading and providing formative feedback to students.

The goal of this workshop is to bring together the existing community of researchers working on Infrastructure Design for Data-Intensive Research in Computer Science Education and a community of Learning at Scale researchers focused on Computer Science Education. While both communities share many similar goals and could greatly benefit from each other work, the interaction between the communities is small. We hope that the proposed workshop will be instrumental in bringing together like-minded researchers from different communities, establishing collaboration, and expanding the scope of infrastructure project to address critical scaling issues.

Prior work suggests that novice programmers are greatly impacted by the feedback provided by their programming environments. While some research has examined the impact of feedback on student learning in programming, there is no work (to our knowledge) that examines the impact of adaptive immediate feedback within programming environments on students' desire to persist in computer science (CS). In this paper, we integrate an adaptive immediate feedback (AIF) system into a block-based programming environment. Our AIF system is novel because it provides personalized positive and corrective feedback to students in real time as they work. In a controlled pilot study with novice high-school programmers, we show that our AIF system significantly increased students' intentions to persist in CS, and that students using AIF had greater engagement (as measured by their lower idle time) compared to students in the control condition. Further, we found evidence that the AIF system may improve student learning, as measured by student performance in a subsequent task without AIF. In interviews, students found the system fun and helpful, and reported feeling more focused and engaged. We hope this paper spurs more research on adaptive immediate feedback and the impact of programming environments on students' intentions to persist in CS.

While programming, novices often lack the ability to effectively seek help, such as when to ask for a hint or feedback. Students may avoid help when they need it, or abuse help to avoid putting in effort, and both behaviors can impede learning. In this paper we present two main contributions. First, we investigated log data from students working in a programming environment that offers automated hints, and we propose a taxonomy of unproductive help-seeking behaviors in programming. Second, we used these findings to design a novel user interface for hints that subtly encourages students to seek help with the right frequency, estimated with a data-driven algorithm. We conducted a pilot study to evaluate our data-driven (DD) hint display, compared to a traditional interface, where students request hints on-demand as desired. We found students with the DD display were less than half as likely to engage in unproductive help-seeking, and we found suggestive evidence that this may improve their learning.

Students often get stuck when programming independently, and need help to progress. Existing, automated feedback can help students progress, but it is unclear whether it ultimately leads to learning. We present Step Tutor, which helps struggling students during programming by presenting them with relevant, step-by-step examples. The goal of Step Tutor is to help students progress, and engage them in comparison, reflection, and learning. When a student requests help, Step Tutor adaptively selects an example to demonstrate the next meaningful step in the solution. It engages the student in comparing "before" and "after" code snapshots, and their corresponding visual output, and guides them to reflect on the changes. Step Tutor is a novel form of help that combines effective aspects of existing support features, such as hints and Worked Examples, to help students both progress and learn. To understand how students use Step Tutor, we asked nine undergraduate students to complete two programming tasks, with its help, and interviewed them about their experience. We present our qualitative analysis of students' experience, which shows us why and how they seek help from Step Tutor, and Step Tutor's affordances. These initial results suggest that students perceived that Step Tutor accomplished its goals of helping them to progress and learn.

In this paper, we introduce ProgSnap2, a standardized format for logging programming process data. ProgSnap2 is a tool for computing education researchers, with the goal of enabling collaboration by helping them to collect and share data, analysis code, and data-driven tools to support students. We give an overview of the format, including how events, event attributes, metadata, code snapshots and external resources are represented. We also present a case study to evaluate how ProgSnap2 can facilitate collaborative research. We investigated three metrics designed to quantify students' difficulty with compiler errors - the Error Quotient, Repeated Error Density and Watwin score - and compared their distributions and ability to predict students' performance. We analyzed five different ProgSnap2 datasets, spanning a variety of contexts and programming languages. We found that each error metric is mildly predictive of students' performance. We reflect on how the common data format allowed us to more easily investigate our research questions.

Students working on programming homework do not receive the same level of support as in the classroom, relying primarily on automated feedback from test cases. One low-effort way to provide more support is by prompting students to compare their solution to an instructor's solution, but it is unclear the best way to design such prompts to support learning. We designed and deployed a randomized controlled trial during online programming homework, where we provided students with an instructor's solution, and randomized whether they were prompted to compare their solution to the instructor's, to fill in the blanks for a written explanation of the instructor's solution, to do both, or neither. Our results suggest that these prompts can effectively engage students in reflecting on instructor solutions, although the results point to design trade-offs between the amount of effort that different prompts require from students and instructors, and their relative impact on learning.

The analysis of data plays an increasingly critical role in computing education research, enabled by more and larger datasets, more powerful analysis techniques and better infrastructure for sharing. This panel brings together four panellists at various stages of work involving the collection and analysis of large datasets in different fields of computing education. The panellists will each discuss the current state of their work, the unique aspects of their data, and how that data fits into the larger landscape of computing education and research. Panellists will be asked to explain how they are employing AI and data mining techniques to learn about learners, the research methods they have used to make this happen, and any significant key findings they have discovered through this processes. The panel will discuss emerging topics, including: going beyond log data, handling global-scale datasets, efficiently collaborating with cross-dataset analysis, and ethical and privacy considerations. After the panelists present (5 minutes each), the moderator will pose follow-up questions and invite the audience to pose additional questions or provide other feedback. Key takeaways will include how data mining and artificial intelligence can contribute to improved insight and learning gains and how the larger computer education community can participate in data collection or analysis.

This paper introduces Crescendo, a self-paced programming practice environment that combines the block-based and visual, interactive programming of Snap!, with the structured practices commonly found in Drill-and-Practice Environments. Crescendo supports students with Parsons problems to reduce problem complexity, Use-Modify-Create task progressions to gradually introduce new programming concepts, and automated feedback and assessment to support learning. In this work, we report on our experience deploying Crescendo in a programming camp for middle school students, as well as in an introductory university course for non-majors. Our initial results from field observations and log data suggest that the support features in Crescendo kept students engaged and allowed them to progress through programming concepts quickly. However, some students still struggled even with these highly-structured problems, requiring additional assistance, suggesting that even strong scaffolding may be insufficient to allow students to progress independently through the tasks.

Gadolinium chelates have become an indispensable tool for the production of contrast enhanced MRI since their introduction into the clinic. A number of gadolinium contrast agents (GdCAs) have been successfully translated into the clinic, however recently some of these have been restricted due to safety concerns. The macrocyclic GdCAs have been shown to be highly stable and have a good safety record; this GdCA design provides a template for the development of new agents with higher relaxivity. This chapter explores the important design considerations for the production of high relaxivity, high stability GdCAs and examines some recent examples that have undergone clinical trials. These examples focus on different methods of increasing the molecular weight of the GdCA to slow tumbling in solution, which improves relaxivity whilst retaining the high stability chelate core. The incorporation of hydrophilic arms that further increase the molecular weight of the GdCA through association of water molecules has been shown to be effective in this goal and does not significantly impact upon the biodistribution of the GdCA.

Data-driven programming hints are a scalable way to support students when they are stuck by automatically offering suggestions and identifying errors. However, few classroom studies have investigated data-driven hints’ impact on students’ performance and learning. In this work, we ran a controlled experiment with 241 students in an authentic classroom setting, comparing students who learned with and without hints. We found no evidence that hints improved student performance or learning overall, and we discuss possible reasons why.

International audience; In this study a bispidine ligand has been applied to the complexation of gallium(III) and radiolabelled with gallium-68 for the first time. Despite its 5-coordinate nature, the resulting complex is stable in serum for over two hours, demonstrating a ligand system well matched to the imaging window of gallium-68 positron emission tomography (PET). To show the versatility of the bispidine ligand and its potential use in PET, the bifunctional chelator was conjugated to a porphyrin, producing a PET/PDT-theranostic, which showed the same level of stability to serum as the non-conjugated gallium-68 complex. The PET/PDT complex killed >90 % of HT-29 cells upon light irradiation at 50 mu m. This study shows bispidines have the versatility to be used as a ligand system for gallium-68 in PET.

A selective fluorescent probe for Zn(ii), AQA-F, has been synthesized. AQA-F exhibits a ratiometric shift in emission of up to 80 nm upon binding Zn(ii) ([AQA-F] = 0.1 mM, [Zn(ii)Cl2] = 0-300 μM). An enhancement of quantum yield from Φ = 4.2% to Φ = 35% is also observed. AQA-F has a binding constant, Kd = 15.2 μM with Zn(ii). This probe has been shown to respond to endogenous Zn(ii) levels in vitro in prostate and prostate cancer cell lines. [18F]AQA-F has been synthesized with a radiochemical yield of 8.6% and a radiochemical purity of 97% in 88 minutes. AQA-F shows the potential for a dual modal PET/fluorescence imaging probe for Zn(ii).

Gallium-68 (68Ga) has been the subject of increasing interest for its potential in the production of radiotracers for diagnosis of diseases. In this work we report the complexation of 68Ga by the amino acid based tripodal chelate H3Dpaa, and two bifunctional derivatives, H3Dpaa.dab and H4Dpaa.ga, under a range of conditions with particular emphasis on the rapid complexation of 68Ga at pH 7.4. 100 μM H3Dpaa achieved a radiochemical yield of 95% at pH 7.4 in 5 minutes at 37 °C. The bifunctional derivatives H4Dpaa.ga and H3Dpaa.dab achieved 94% and 84% radiochemical yields, respectively, under the same conditions. The resulting Ga(III) complexes show thermodynamic stabilities of log KGaDpaa = 18.53, log KGaDpaa.dab = 22.08, log KGaDpaa.ga = 18.36. Unfortunately, the resulting radiolabelled species do not present sufficient serum stability for in vivo application. Herein we show a flexible synthesis for bifunctional chelators based on amino acids that rapidly complex 68Ga under physiological conditions.

A theranostic conjugate for use as a positron emission tomography (PET) radiotracer and as a photosensitiser for photodynamic therapy (PDT) has been synthesised. A water-soluble porphyrin was coupled with the bifunctional chelate, H4Dpaa.ga. This conjugate is capable of rapid 68Ga complexation under physiological conditions; with 93% and 80% radiochemical yields achieved, at pH 4.5 and pH 7.4 respectively, in 15 min at 25 °C. Photocytotoxicity was evaluated on HT-29 cells and showed the conjugate was capable of >50% cell death at 50 μM upon irradiation with light, while causing minimal toxicity in the absence of light (>95% cell survival).

Programming games provide players opportunities to practice and learn the fundamentals of coding in engaging ways. Many games have players program in block-based languages similar to environments like Scratch and Snap! as a means to scaffold student learning and reduce syntax errors. Block-based environments (BBEs) have been praised for their open-ended, constructionist designs allowing students to develop what they wish, express themselves, and explore the possibilities of the system. However, programming games tend to be more linear, usually designed as a fixed series of puzzles. We present Resource Rush, a game designed to resemble BBEs and present users with a game world that allows users to learn the fundamentals of programming in an open-ended game environment.

Parsons problems are program puzzles, where students piece together code fragments to construct a program. Similar to block-based programming environments, Parsons problems eliminate the need to learn syntax. Parsons problems have been shown to improve learning efficiency when compared to writing code or fixing incorrect code in lab studies, or as part of a larger curriculum. In this study, we directly compared Parsons problems with block-based programming assignments in classroom settings. We hypothesized that Parsons problems would improve students' programming efficiency on the lab assignments where they were used, without impacting performance on the subsequent, related homework or the later programming project. Our results confirmed our hypothesis, showing that on average Parsons problems took students about half as much time to complete compared to equivalent programming problems. At the same time, we found no evidence to suggest that students performed worse on subsequent assignments, as measured by performance and time on task. The results indicate that the effectiveness of Parsons problems is not simply based on helping students avoid syntax errors. We believe this is because Parsons problems dramatically reduce the programming solution space, letting students focus on solving the problem rather than having to solve the combined problem of devising a solution, searching for needed components, and composing them together.

A growing body of work has explored how to automatically generate hints for novice programmers, and many programming environments now employ these hints. However, few studies have investigated the efficacy of automated programming hints for improving performance and learning, how and when novices find these hints beneficial, and the tradeoffs that exist between different types of hints. In this work, we explored the efficacy of next-step code hints with 2 complementary features: textual explanations and self-explanation prompts. We conducted two studies in which novices completed two programming tasks in a block-based programming environment with automated hints. In Study 1, 10 undergraduate students completed 2 programming tasks with a variety of hint types, and we interviewed them to understand their perceptions of the affordances of each hint type. For Study 2, we recruited a convenience sample of participants without programming experience from Amazon Mechanical Turk. We conducted a randomized experiment comparing the effects of hints' types on learners' performance and performance on a subsequent task without hints. We found that code hints with textual explanations significantly improved immediate programming performance. However, these hints only improved performance in a subsequent post-test task with similar objectives, when they were combined with self-explanation prompts. These results provide design insights into how automatically generated code hints can be improved with textual explanations and prompts to self-explain, and provide evidence about when and how these hints can improve programming performance and learning.

Automated hints, a powerful feature of many programming environments, have been shown to improve students' performance and learning. New methods for generating these hints use historical data, allowing them to scale easily to new classrooms and contexts. These scalable methods often generate next-step, code hints that suggest a single edit for the student to make to their code. However, while these code hints tell the student what to do, they do not explain why, which can make these hints hard to interpret and decrease students' trust in their helpfulness. In this work, we augmented code hints by adding adaptive, textual explanations in a block-based, novice programming environment. We evaluated their impact in two controlled studies with novice learners to investigate how our results generalize to different populations. We measured the impact of textual explanations on novices' programming performance. We also used quantitative analysis of log data, self-explanation prompts, and frequent feedback surveys to evaluate novices' understanding and perception of the hints throughout the learning process. Our results showed that novices perceived hints with explanations as significantly more relevant and interpretable than those without explanations, and were also better able to connect these hints to their code and the assignment. However, we found little difference in novices' performance. Our results suggest that explanations have the potential to make code hints more useful, but it is unclear whether this translates into better overall performance and learning.

Research in a variety of domains has shown that viewing worked examples (WEs) can be a more efficient way to learn than solving equivalent problems. We designed a Peer Code Helper system to display WEs, along with scaffolded self-explanation prompts, in a block-based, novice programming environment called \snap. We evaluated our system during a high school summer camp with 22 students. Participants completed three programming problems with access to WEs on either the first or second problem. We found that WEs did not significantly impact students' learning, but may have impacted students' intrinsic cognitive load, suggesting that our WEs with scaffolded prompts may be an inherently different learning task. Our results show that WEs saved students time on initial tasks compared to writing code, but some of the time saved was lost in subsequent programming tasks. Overall, students with WEs completed more tasks within a fixed time period, but not significantly more. WEs may improve students' learning efficiency when programming, but these effects are nuanced and merit further study.

Tinkering has been shown to have a positive influence on students in open-ended making activities. Open-ended programming assignments in block-based programming resemble making activities in that both of them encourage students to tinker with tools to create their own solutions to achieve a goal. However, previous studies of tinkering in programming discussed tinkering as a broad, ambiguous term, and investigated only self-reported data. To our knowledge, no research has studied student tinkering behaviors while solving problems in block-based programming environments. In this position paper, we propose a definition for tinkering in block-based programming environments as a kind of behavior that students exhibit when testing, exploring, and struggling during problem-solving. We introduce three general categories of tinkering behaviors (test-based, prototype-based, and construction-based tinkering) derived from student data, and use case studies to demonstrate how students exhibited these behaviors in problem-solving. We created the definitions using a mixed-methods research design combining a literature review with data-driven insights from submissions of two open-ended programming assignments in iSnap, a block-based programming environment. We discuss the implication of each type of tinkering behavior for learning. Our study and results are the first in this domain to define tinkering based on student behaviors in a block-based programming environment.

In the domain of programming, a growing number of algorithms automatically generate data-driven, next-step hints that suggest how students should edit their code to resolve errors and make progress. While these hints have the potential to improve learning if done well, few evaluations have directly assessed or compared the quality of different hint generation approaches. In this work, we present the QualityScore procedure, a novel method for automatically evaluating and comparing the quality of next-step programming hints using expert ratings. We first demonstrate that the automated QualityScore ratings agree with experts’ manual ratings. We then use the QualityScore procedure to compare the quality of six data-driven, next-step hint generation algorithms using two distinct programming datasets in two different programming languages. Our results show that there are large and significant differences between the quality of the six algorithms and that these differences are relatively consistent across datasets and problems. We also identify situations where the six algorithms struggle to produce high-quality hints, and we suggest ways that future work might address these challenges. We make our methods and data publicly available and encourage researchers to use the QualityScore procedure to evaluate additional algorithms and benchmark them against our results.

Summary Fluorescence imaging has gathered interest over the recent years for its real-time response and high sensitivity. Developing probes for this modality has proven to be a challenge. Quantum dots (QDs) are colloidal nanoparticles that possess unique optical and electronic properties due to quantum confinement effects, whose excellent optical properties make them ideal for fluorescence imaging of biological systems. By selectively controlling the synthetic methodologies it is possible to obtain QDs that emit in the first (650–950 nm) and second (1000–1400 nm) near infra-red (NIR) windows, allowing for superior imaging properties. Despite the excellent optical properties and biocompatibility shown by some NIR QDs, there are still some challenges to overcome to enable there use in clinical applications. In this review, we discuss the latest advances in the application of NIR QDs in preclinical settings, together with the synthetic approaches and material developments that make NIR QDs promising for future biomedical applications., Graphical abstract, Optical Materials; Biomedical Engineering; Engineering; Materials Science; Materials Chemistry

A key part of the development of metal based Positron Emission Tomography probes is the chelation of the radiometal. In this review the recent developments in the chelation of four positron emitting radiometals, 68Ga, 64Cu, 89Zr and 44Sc, are explored. The factors that effect the chelation of each radio metal and the ideal ligand system will be discussed with regards to high in vivo stability, complexation conditions, conjugation to targeting motifs and complexation kinetics. A series of cyclic, cross-bridged and acyclic ligands will be discussed, such as CP256 which forms stable complexes with 68Ga under mild conditions and PCB-TE2A which has been shown to form a highly stable complex with 64Cu. 89Zr and 44Sc have seen significant development in recent years with a number of chelates being applied to each metal – eight coordinate di-macrocyclic terephthalamide ligands were found to rapidly produce more stable complexes with 89Zr than the widely used DFO.

A selective fluorescent probe for Zn(ii), AQA-F, has been synthesized. AQA-F exhibits a ratiometric shift in emission of up to 80 nm upon binding Zn(ii) ([AQA-F] = 0.1 mM, [Zn(ii)Cl2] = 0-300 μM). An enhancement of quantum yield from Φ = 4.2% to Φ = 35% is also observed. AQA-F has a binding constant, Kd = 15.2 μM with Zn(ii). This probe has been shown to respond to endogenous Zn(ii) levels in vitro in prostate and prostate cancer cell lines. [18F]AQA-F has been synthesized with a radiochemical yield of 8.6% and a radiochemical purity of 97% in 88 minutes. AQA-F shows the potential for a dual modal PET/fluorescence imaging probe for Zn(ii).

iSnap is a block-based programming environment that supports struggling students with on-demand hints and error-checking feedback. iSnap is an extension of Snap!, a creative and novice-friendly programming environment, used in the Beauty and Joy of Computing (BJC) AP CS Principles curriculum. iSnap is designed to support the open-ended, exploratory programming problems of BJC, while adapting to many possible student solutions. When students ask iSnap for help, it highlights possible errors in their code and suggests next steps they can make. Hints are presented visually, right alongside students/ code, making them easy to interpret and implement. iSnap/s hints are generated automatically from student data, so no teacher input is required to create them, making iSnap appropriate for both new and experienced instructors. The demonstration will showcase iSnap/s hints on a variety of assignments and explain how the algorithm is working behind the scenes to generate data-driven hints. It will also include an overview of the results from two years of research with iSnap on how students seek and use programming help. A key objective of this demonstration is to solicit feedback from SIGCSE attendees on the design of iSnap as we work to make the system ready for deployment in classrooms. More information on iSnap can be found at http://go.ncsu.edu/isnap.

Instructional supports (Supports) help students learn more effectively in intelligent tutoring systems and gamified educational environments. However, the implementation and success of Supports vary by environment. We explored Support design in an educational programming game, BOTS, implementing three different strategies: instructional text (Text), worked examples (Examples) and buggy code (Bugs). These strategies are adapted from promising Supports in other domains and motivated by established educational theory. We evaluated our Supports through a pilot study with middle school students. Our results suggest Bugs may be a promising strategy, as demonstrated by the lower completion time and solution code length in assessment puzzles. We end reflecting on our design decisions providing recommendations for future iterations. Our motivations, design process, and study's results provide insight into the design of Supports for programming games.

In the domain of programming, intelligent tutoring systems increasingly employ data-driven methods to automate hint generation. Evaluations of these systems have largely focused on whether they can reliably provide hints for most students, and how much data is needed to do so, rather than how useful the resulting hints are to students. We present a method for evaluating the quality of data-driven hints and how their quality is impacted by the data used to generate them. Using two datasets, we investigate how the quantity of data and the source of data (whether it comes from students or experts) impact one hint generation algorithm. We find that with student training data, hint quality stops improving after 15–20 training solutions and can decrease with additional data. We also find that student data outperforms a single expert solution but that a comprehensive set of expert solutions generally performs best.

Block-based programming environments make learning to program easier by allowing learners to focus on concepts rather than syntax. However, these environments offer little support when learners encounter difficulty with programming concepts themselves, especially in the absence of instructors. Textual programming environments increasingly use AI and data mining to provide intelligent, adaptive support for students, similar to human tutoring, which has been shown to improve performance and learning outcomes. In this position paper, we argue that block-based programming environments should also include these features. This paper gives an overview of promising research in intelligent support for programming and highlights the challenges and opportunities for applying this work to block-based programming.

This showpiece will present iSnap, an extension of the block-based, novice programming environment Snap!, which supports struggling students by providing on-demand hints and feedback that help them complete programming assignments. iSnap extends the existing syntactic scaffolding offered by block-based programming to additionally support the implementation of programming tasks. Research on iSnap has explored questions of how visual programming environments can better support learners, the impact of this support, and how learners seek and use computer-based help. The showpiece will consist of an interactive demonstration of iSnap, including the user interface experienced by students and the data-driven algorithm used to automatically generate the programming feedback.

When novice students encounter difficulty when learning to program, some can seek help from instructors or teaching assistants. This one-on-one tutoring is highly effective at fostering learning, but busy instructors and large class sizes can make expert help a scarce resource. Increasingly, programming environments attempt to imitate this human support by providing students with hints and feedback. In order to design effective, computer-based help, it is important to understand how and why students seek and avoid help when programming, and how this process differs when the help is provided by a human or a computer. We explore these questions through a qualitative analysis of 15 students' interviews, in which they reflect on solving two programming problems with human and computer help. We discuss implications for help design and present hypotheses on students' help-seeking behavior.