English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Block-based visual programming environments are increasingly used tointroduce computing concepts to beginners. Given that programming tasks areopen-ended and conceptual, novice students often struggle when learning inthese environments. AI-driven programming tutors hold great promise inautomatically assisting struggling students, and need several components torealize this potential. We investigate the crucial component of studentmodeling, in particular, the ability to automatically infer students'misconceptions for predicting (synthesizing) their behavior. We introduce anovel benchmark, StudentSyn, centered around the following challenge: For agiven student, synthesize the student's attempt on a new target task afterobserving the student's attempt on a fixed reference task. This challenge isakin to that of program synthesis; however, instead of synthesizing a{solution} (i.e., program an expert would write), the goal here is tosynthesize a {student attempt} (i.e., program that a given student wouldwrite). We first show that human experts (TutorSS) can achieve high performanceon the benchmark, whereas simple baselines perform poorly. Then, we develop twoneuro/symbolic techniques (NeurSS and SymSS) in a quest to close this gap withTutorSS.

From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks