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We explore the role of quizzes in elementary visual programming domains popularly used for K-8 computing education. Prior work has studied various quiz types, such as fill-in-the-gap write-code questions. However, the overall impact of these quizzes is unclear: studies often show utility in the learning phase when enhanced with quizzes, though limited transfer of utility in the post-learning phase. In this paper, we aim to better understand the impact of different quiz types and whether quizzes focusing on diverse skills (e.g., code debugging and task design) would have higher utility. We design a study with Hour of Code: Maze Challenge by code.org as the base curriculum, interleaved with different quiz types. Specifically, we examine two learning groups: (i) HoC-ACE with diverse quizzes including solution tracing, code debugging, code equivalence, and task design; (ii) HoC-Fill with simple quizzes on solution finding. We conducted a large-scale study with 405 students in grades 6--7. Our results highlight that the curriculum enhanced with richer quizzes led to higher utility during the post-learning phase.

Computational thinking (CT) and problem-solving skills are increasingly integrated into K-8 school curricula worldwide. Consequently, there is a growing need to develop reliable assessments for measuring students' proficiency in these skills. Recent works have proposed tests for assessing these skills across various CT concepts and practices, in particular, based on multi-choice items enabling psychometric validation and usage in large-scale studies. Despite their practical relevance, these tests are limited in how they measure students' computational creativity, a crucial ability when applying CT and problem solving in real-world settings. In our work, we have developed ACE, a novel test focusing on the three higher cognitive levels in Bloom's Taxonomy, i.e., Analyze, Evaluate, and Create. ACE comprises a diverse set of 7x3 multi-choice items spanning these three levels, grounded in elementary block-based visual programming. We evaluate the psychometric properties of ACE through a study conducted with 371 students in grades 3-7 from 10 schools. Based on several psychometric analysis frameworks, our results confirm the reliability and validity of ACE. Our study also shows a positive correlation between students' performance on ACE and performance on Hour of Code: Maze Challenge by Code.org.

Block-based visual programming environments play a critical role in introducing computing concepts to K-12 students. One of the key pedagogical challenges in these environments is in designing new practice tasks for a student that match a desired level of difficulty and exercise specific programming concepts. In this paper, we formalize the problem of synthesizing visual programming tasks. In particular, given a reference visual task $\task^{in}$ and its solution code $\code^{in}$, we propose a novel methodology to automatically generate a set $\{(\task^{out}, \code^{out})\}$ of new tasks along with solution codes such that tasks $\task^{in}$ and $\task^{out}$ are conceptually similar but visually dissimilar. Our methodology is based on the realization that the mapping from the space of visual tasks to their solution codes is highly discontinuous; hence, directly mutating reference task $\task^{in}$ to generate new tasks is futile. Our task synthesis algorithm operates by first mutating code $\code^{in}$ to obtain a set of codes $\{\code^{out}\}$. Then, the algorithm performs symbolic execution over a code $\code^{out}$ to obtain a visual task $\task^{out}$; this step uses the Monte Carlo Tree Search (MCTS) procedure to guide the search in the symbolic tree. We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com.

Inverse reinforcement learning (IRL) enables an agent to learn complex behavior by observing demonstrations from a (near-)optimal policy. The typical assumption is that the learner's goal is to match the teacher’s demonstrated behavior. In this paper, we consider the setting where the learner has its own preferences that it additionally takes into consideration. These preferences can for example capture behavioral biases, mismatched worldviews, or physical constraints. We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner’s preferences. We design learner-aware teaching algorithms and show that significant performance improvements can be achieved over learner-agnostic teaching.

In this paper we propose using the compression method, Shannon-Fano-Elias coding, for encryption. Shannon-Fano-Elias codes lend themselves for encryption because code-words depend on the order in which the symbols that need to be coded are written and it does not matter if the probability mass function of the symbols is known to everyone. If there are n symbols then there are n! orderings, each leading to a new code. Using an ordering as a key for encryption for small n leads to a weak encryption scheme. We therefore propose a new scheme called adaptive Shannon-Fano-Elias code that makes the complexity of attacks exponential in m, where m is the length of the string being compressed. Since m is usually very large (> 220), the security of our scheme is very high. The main reason why our scheme's security depends on m is the fact that all attacks require the ciphertext to be scanned from left to right.