Key Action Extraction for Learning Analytics

Proceedings of: 7th European Conference on Technology Enhanced Learning (EC-TEL 2012): 21st Century Learning for 21st Century Skills. Saarbrücken, Germany, September 18-21, 2012.Analogous to keywords describing the important and relevant content of a document we extract key actions from learners' usage data assuming that they represent important and relevant parts of their learning behaviour. These key actions enable the teachers to better understand the dynamics in their classes and the problems that occur while learning. Based on these insights, teachers can intervene directly as well as improve the quality of their learning material and learning design. We test our approach on usage data collected in a large introductory C programming course at a university and discuss the results based on the feedback of the teachers.Work partially funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 231396 (ROLE project), the Learn3 project (TIN2008-05163/TSI), the eMadrid project (S2009/TIC-1650), and the Acci´on Integrada DE2009-0051.Publicad

TrueSet: Faster Verifiable Set Computations

Verifiable computation (VC) enables thin clients to efficiently verify the computational results produced by a powerful server. Although VC was initially considered to be mainly of theoretical interest, over the last two years, impressive progress has been made on implementing VC. Specifically, we now have open-source implementations of VC systems that can handle all classes of computations expressed either as circuits or in the RAM model. However, despite this very encouraging progress, new enhancements in the design and implementation of VC protocols are required in order to achieve truly practical VC for real-world applications. In this work, we show that for functionalities that can be expressed efficiently in terms of set operations (e.g., a subset of SQL queries) VC can be enhanced to become drastically more practical: we present the design and prototype implementation of a novel VC scheme that achieves orders of magnitude speed-up in comparison with the state of the art. Specifically, we build and evaluate TRUESET, a system that can verifiably compute any polynomial-time function expressed as a circuit consisting of \set gates such as union, intersection, difference and set cardinality. Moreover, TRUESET supports hybrid circuits consisting of both set gates and traditional arithmetic gates. Therefore, it does not lose any of the expressiveness of the previous schemes|this also allows the user to choose the most efficient way to represent different parts of a computation. By expressing set computations as polynomial operations and introducing a novel Quadratic Polynomial Program technique, TRUESET achieves prover performance speed-up ranging from 30x to 150x and yields up to 97% evaluation key size reduction

Usage Pattern Recognition in Student Activities

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.This paper presents an approach of collecting contextualized attention metadata combined from inside as well as outside a LMS and analyzing them to create feedback about the student activities for the teaching staff. Two types of analyses were run on the collected data: first, key actions were extracted to identify usage patterns and tendencies throughout the whole course and then usage statistics and patterns were identified for some key actions in more detail. Results of both analyses were visualized and presented to the teaching staff for evaluation.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007- 2013) under grant agreement no 231396 (ROLE project). Work was also partially funded by the Learn3 project (TIN2008-05163/TSI), the eMadrid project (S2009/TIC-1650), and the Acción Integrada DE2009-0051

Short Paper: Blockcheck the Typechain

Recent efforts have sought to design new smart contract programming languages that make writing blockchain programs safer. But programs on the blockchain are beholden only to the safety properties enforced by the blockchain itself: even the strictest language-only properties can be rendered moot on a language-oblivious blockchain due to inter-contract interactions. Consequently, while safer languages are a necessity, fully realizing their benefits necessitates a language-aware redesign of the blockchain itself. To this end, we propose that the blockchain be viewed as a typechain: a chain of typed programs-not arbitrary blocks-that are included iff they typecheck against the existing chain. Reaching consensus, or blockchecking, validates typechecking in a byzantine fault-tolerant manner. Safety properties traditionally enforced by a runtime are instead enforced by a type system with the aim of statically capturing smart contract correctness. To provide a robust level of safety, we contend that a typechain must minimally guarantee (1) asset linearity and liveness, (2) physical resource availability, including CPU and memory, (3) exceptionless execution, or no early termination, (4) protocol conformance, or adherence to some state machine, and (5) inter-contract safety, including reentrancy safety. Despite their exacting nature, typechains are extensible, allowing for rich libraries that extend the set of verified properties. We expand on typechain properties and present examples of real-world bugs they prevent

Understanding Learners’ Behaviors in Serious Games

Best paper awardInternational audienceUnderstanding play traces resulting from the learner’s activity in serious games is a challenged research area. Especially, when the serious game is characterized by a large state space and a large amount of free interactions, the play traces become complex and thus hard to analyze and to interpret by teachers. In this paper, we present a framework that assists designers to build a model of an expert’s solving process semi-automatically. Based on this model, we propose an algorithm that analyzes player’s traces in order to generate pedagogical labels about the learner’s behavior. We carried out an experimental study which aimed to evaluate the effectiveness of the labeling algorithm. From a usability point of view, we also use the experiment to validate the acceptation and readability of pedagogical labels by the teachers

Securify: practical security analysis of smart contracts

Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations