Providing Service-based Personalization in an Adaptive Hypermedia System

Adaptive hypermedia is one of the most popular approaches of personalized information access. When the field started to emerge, the expectation was that soon nearly all published hypermedia content could be adapted to the needs, preferences, and abilities of its users. However, after a decade and a half, the gap between the amount of total hypermedia content available and the amount of content available in a personalized way is still quite large.In this work we are proposing a novel way of speeding the development of new adaptive hypermedia systems. The gist of the approach is to extract the adaptation functionality out of the adaptive hypermedia system, encapsulate it into a standalone system, and offer adaptation as a service to the client applications. Such a standalone adaptation provider reduces the development of adaptation functionality to configuration and compliance and as a result creates new adaptive systems faster and helps serve larger user populations with adaptively accessible content.To empirically prove the viability of our approach, we developed PERSEUS - server of adaptation functionalities. First, we confirmed that the conceptual design of PERSEUS supports realization of a several of the widely used adaptive hypermedia techniques. Second, to demonstrate that the extracted adaptation does not create a significant computational bottleneck, we conducted a series of performance tests. The results show that PERSEUS is capable of providing a basis for implementing computationally challenging adaptation procedures and compares well with alternative, not-encapsulated adaptation solutions. As a result, even on modest hardware, large user populations can be served content adapted by PERSEUS

Addictive links: The motivational value of adaptive link annotation

Adaptive link annotation is a popular adaptive navigation support technology. Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. In this paper, we present our exploration of a lesser known effect of adaptive annotation, its ability to significantly increase students' motivation to work with non-mandatory educational content. We explored this effect and confirmed its significance in the context of two different adaptive hypermedia systems. The paper presents and discusses the results of our work

Investigating Automated Student Modeling in a Java MOOC

With the advent of ubiquitous web, programming is no longer a sole prerogative of computer science schools. Scripting languages are taught to wider audiences and programming has become a flag post of any technology related program. As more and more students are exposed to coding, it is no longer a trade of the select few. As a result, students who would not opt for a coding class a decade ago are in a position of having to learn a rather difficult subject. The problem of assisting students in learning programming has been explored in several intelligent tutoring systems. The key component of such systems is a student model that keeps track of student progress. In turn, the foundation of a student model is a domain model – a vocabulary of skills (or concepts) that structures the representation of student knowledge. Building domain models for programming is known as a complicated task. In this paper we explore automated approaches for extracting domain models for learning programming languages and modeling student knowledge in the process of solving programming exercises. We evaluate the validity of this approach using large volume of student code submission data from a MOOC on introductory Java programming

The value of adaptive link annotation in e-learning: A study of a portal-based approach

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 21st ACM conference on Hypertext and hypermedia, http://dx.doi.org/10.1145/1810617.1810657Adaptive link annotation is one of the most popular adaptive educational hypermedia techniques. It has been widely studied and demonstrated its ability to help students to acquire knowledge faster, improve learning outcomes, reduce navigation overhead, increase motivation, and encourage the beneficial non-sequential navigation. However, almost all studies of adaptive link annotation have been performed in the context of dedicated adaptive educational hypermedia systems. The role of this technique in the context of widely popular learning portals has not yet been demonstrated. In this paper, we attempt to fill this gap by investigating the value of adaptive navigation support embedded into the learning portal. We compare the effect of portal-based adaptive navigation support to both the effect of the adaptive navigation support in adaptive educational hypermedia systems and to non-adaptive learning portals.This work is supported by National Science Foundation under Grant IIS-0447083, Spanish Ministry of Science and Education (TIN2007-64718) and the Comunidad Autónoma de Madrid (S2009/TIC-1650

Plagiarism in Take-home Exams: Help-seeking, Collaboration, and Systematic Cheating

Due to the increased enrollments in Computer Science education programs, institutions have sought ways to automate and streamline parts of course assessment in order to be able to invest more time in guiding students' work. This article presents a study of plagiarism behavior in an introductory programming course, where a traditional pen-and-paper exam was replaced with multiple take-home exams. The students who took the take-home exam enabled a software plugin that recorded their programming process. During an analysis of the students' submissions, potential plagiarism cases were highlighted, and students were invited to interviews. The interviews with the candidates for plagiarism highlighted three types of plagiarism behaviors: help-seeking, collaboration, and systematic cheating. Analysis of programming process traces indicates that parts of such behavior are detectable directly from programming process data.Peer reviewe

Stereotype modeling for problem-solving performance predictions in moocs and traditional courses

Stereotypes are frequently used in real life to classify students according to their performance in class. In literature, we can find many references to weaker students, fast learners, struggling students, etc. Given the lack of detailed data about students, these or other kinds of stereotypes could be potentially used for user modeling and personalization in the educational context. Recent research in MOOC context demonstrated that data-driven learner stereotypes could work well for detecting and preventing student dropouts. In this paper, we are exploring the application of stereotype-based modeling to a more challenging task - predicting student problemsolving and learning in two programming courses and two MOOCs. We explore traditional stereotypes based on readily available factors like gender or education level as well as some advanced data-driven approaches to group students based on their problem-solving behavior. Each of the approaches to form student stereotype cohorts is validated by comparing models of student learning: do students in different groups learn differently? In the search for the stereotypes that could be used for adaptation, the paper examines ten approaches. We compare the performance of these approaches and draw conclusions for future research

Knowledge Tracing with Sequential Key-Value Memory Networks

Can machines trace human knowledge like humans? Knowledge tracing (KT) is a fundamental task in a wide range of applications in education, such as massive open online courses (MOOCs), intelligent tutoring systems, educational games, and learning management systems. It models dynamics in a student's knowledge states in relation to different learning concepts through their interactions with learning activities. Recently, several attempts have been made to use deep learning models for tackling the KT problem. Although these deep learning models have shown promising results, they have limitations: either lack the ability to go deeper to trace how specific concepts in a knowledge state are mastered by a student, or fail to capture long-term dependencies in an exercise sequence. In this paper, we address these limitations by proposing a novel deep learning model for knowledge tracing, namely Sequential Key-Value Memory Networks (SKVMN). This model unifies the strengths of recurrent modelling capacity and memory capacity of the existing deep learning KT models for modelling student learning. We have extensively evaluated our proposed model on five benchmark datasets. The experimental results show that (1) SKVMN outperforms the state-of-the-art KT models on all datasets, (2) SKVMN can better discover the correlation between latent concepts and questions, and (3) SKVMN can trace the knowledge state of students dynamics, and a leverage sequential dependencies in an exercise sequence for improved predication accuracy