Related papers: The Course Difficulty Analysis Cookbook
Describing and analysing learner behaviour using sequential data and analysis is becoming more and more popular in Learning Analytics. Nevertheless, we found a variety of definitions of learning sequences, as well as choices regarding data…
Mobile applications and other integration of information and communication technology (ICT) have become well-known in education to monitor teaching and learning activities. The analysis of student learning through evaluation is a growing…
Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via quantum computing. As in classical learning, quantum learning…
We analyze the curriculum of the early common-years of engineering in our institute using tools of statistical physics of complex networks. Naturally, a course programme is structured in a networked form (temporal dependency and…
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from…
Curriculum learning (CL) aims to improve training by presenting data from "easy" to "hard", yet defining and measuring linguistic difficulty remains an open challenge. We investigate whether human-curated simple language can serve as an…
Context: Tertiary studies are becoming increasingly popular in software engineering as an instrument to synthesise evidence on a research topic in a systematic way. In order to understand and contextualize their findings, it is important to…
Approximate unlearning for session-based recommendation refers to eliminating the influence of specific training samples from the recommender without retraining of (sub-)models. Gradient ascent (GA) is a representative method to conduct…
Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty heuristics with learned difficulty parameters. We also…
Academic performance depends on a multivariable nexus of socio-academic and financial factors. This study investigates these influences to develop effective strategies for optimizing students' CGPA. To achieve this, we reviewed various…
The ability to recognize weakness of students and solving any problem may confront them in timely fashion is always a target of all educational institutions. This study was designed to explore how can predictive and statistical analysis…
At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease development, severe weather, production quality default(s) (items)…
Massive Open Online Courses (MOOCs) focus on manifold subjects, ranging from social sciences over languages to technical skills, and use different means to train the respective skills. MOOCs that are teaching programming skills aim to…
Data-oriented applications, their users, and even the law require data of high quality. Research has divided the rather vague notion of data quality into various dimensions, such as accuracy, consistency, and reputation. To achieve the goal…
Both student retention in higher education and artificial intelligence governance face a common structural challenge: the application of linear regulatory frameworks to complex adaptive systems. Risk-based approaches dominate both domains,…
Collaborative group projects are integral to computer science education, fostering teamwork, problem-solving, and industry-relevant skills. However, assessing individual contributions within group settings remains challenging. Traditional…
As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recently, the idea of mining-based strategies is adopted to…
A curriculum is a planned sequence of learning materials and an effective one can make learning efficient and effective for both humans and machines. Recent studies developed effective data-driven curriculum learning approaches for training…
The surge in the adoption of Intelligent Tutoring Systems (ITSs) in education, while being integral to curriculum-based learning, can inadvertently exacerbate performance gaps. To address this problem, student profiling becomes crucial for…
Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this work, we present CORA, a platform for Continual…