Search results for "learning."

Online anomaly detection using dimensionality reduction techniques for HTTP log analysis

2015

Modern web services face an increasing number of new threats. Logs are collected from almost all web servers, and for this reason analyzing them is beneficial when trying to prevent intrusions. Intrusive behavior often differs from the normal web traffic. This paper proposes a framework to find abnormal behavior from these logs. We compare random projection, principal component analysis and diffusion map for anomaly detection. In addition, the framework has online capabilities. The first two methods have intuitive extensions while diffusion map uses the Nyström extension. This fast out-of-sample extension enables real-time analysis of web server traffic. The framework is demonstrated using …

Making group processes explicit to student

2014

This article considers student learning about group work in the context of project courses where student groups work under realistic expectations. Based on the literature, justice is explicated as a group work concept and regarded as a professional skill that can be practiced. Preliminary student feedback on teaching through continuous discussions on justice are presented together with teacher experiences.

Listwise Collaborative Filtering

2015

Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do). In this paper, we propose a new ranking-oriented CF algorithm, called ListCF. Following the memory-based CF framework, ListCF directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items, and thus differs from previous ranking-oriented memory-based CF algorithms that focus on predicting th…

An intelligent learning support system

2017

Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere e…

Supporting Institutional Awareness and Academic Advising using Clustered Study Profiles

2017

The purpose of academic advising is to help students with developing educational plans that support their academic career and personal goals, and to provide information and guidance on studies. Planning and management of the students’ study path is the main joint activity in advising. Based on a study log of passed courses, we propose to use robust, prototype-based clustering to identify a set of actual study path profiles. Such profiles identify groups of students with similar progress of studies, whose analysis and interpretation can be used for better institutional awareness and to support evidence-based academic advising. A model of automated academic advising system utilizing the possi…

First Year Computing Students' Perceptions of Authenticity in Assessment

2017

The problem of how best to assess student learning is a fundamental one in education. Changes to computer science curricula seek to emphasise teaching practices that promote deep learning through direct, contextual examination of student performance on tasks that resemble those of practitioners, rather than more traditional methods. This kind of "authentic assessment" is becoming more popular as it appears to incorporate employability skills associated with professional practice into the curriculum in a natural way. In this paper, we report on an investigation into how computing students themselves understand the terminology of authentic assessment. We give a brief summary of some of the sa…

Does the Learning Channel Really Matter? - Insights from Commercial Online ICT-training

2017

Evolving ICT has provided new options to participate to training. Online participation has been found to be cost effective, helping people to deal with the time and cost pressures they are facing on their jobs. Previous studies conducted in higher education sector indicates that student satisfaction or learning outcomes does not differ between online and classroom participants. However, little is known what is the situation in commercial ICT-training. This paper studied course feedbacks from courses having both online and classroom participants of a commercial ICT-training provider. Results revealed that the learning channel has no effect on satisfaction, perceived teacher’s substance and t…

Habituating Students to IPR Questions During Creative Project Work

2017

CONSIDERING VARIOUS STAKEHOLDERS’ VIEWS IN THE DESIGN OF A HYBRID STEM LEARNING ENVIRONMENT - PERCEPTIONS FROM FINLAND AND GREECE

2017

Raising young people’s interest in studies and careers related to science, technology, engineering, and mathematics (i.e., STEM) is an important societal concern both at European and global level. We argue that the creation of attractive and engaging STEM learning environments necessitates involvement of learners, educators, parents, and STEM professionals in their design. In this paper, we will present a study in which primary, lower and upper secondary school students, teachers, school directors, parents, and STEM professionals in Finland (n = 27) and Greece (n = 24) were invited in the participatory co-design of a hybrid (virtual, physical, formal, and informal) STEM learning environment…

Recommending Serendipitous Items using Transfer Learning

2018

Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommend…