Measuring the difficulty of activities for adaptive learning

Abstract

An effective adaptive learning system would theoretically maintain learners in a permanent state of flow. In this state, learners are completely focused on activities. To attain this state, the difficulty of learning activities must match learners' skills. To perform this matching, it is essential to define, measure and deeply analyze difficulty. However, very few previous works deal with difficulty in depth. Most commonly, difficulty is defined as a one-dimensional value. This permits ordering activities, but limits the possibilities of deep analysis of activities and learners' performance. This work proposes a new definition of difficulty and a way to measure it. The proposed definition depends on learners' progress on activities over time. This expands the concept of difficulty over a two-dimensional space, also making it drawable. The difficulty graphs provide a rich interpretation with insights into the learning process. A practical case is presented: the PLMan Learning System. This system is formed by a web application and a game to teach Computational Logic. The proposed definition is applied in this context. Measures are taken and analyzed using difficulty graphs. Some examples of these analyses are shown to illustrate the benefits of this proposal. Singularities and interesting spots are easily identified in graphs, providing insights in the activities. This new information lets experts adapt the learning

First Steps Towards Automatically Defining the Difficulty of Maze-Based Programming Challenges

“…Difficulty is generally defined as the commitment taken to effectively perform an operation [11]. Aponte et al [26] claim that the difficulty of a challenge is the probability that the player will fail at it.…”

“…Several approaches to defining difficulty in games already exist, ranging from measuring the difficulty of video games [33], [34] to assessing the difficulty of educational games [11], [35].…”

“…To this end, we must be able to estimate the difficulty of these activities so that teachers-or, ideally, a tool-can generate increasingly challenging learning activities with adaptive difficulty based on the learner's performance. According to flow theory, adequate scaffolding through challenges is key to offering challenges that are neither too difficult nor too easy, consequently causing anxiety or boredom [11].…”

Glimpses Into Real-Life Introduction of Adaptive Learning Technology: A Mixed Methods Research Approach to Personalised Pupil Learning

“…This challenge applies particularly to primary school pupils who have to learn and understand basic principles while at the same time do volume training. The very intention of ALT-mediated volume training is to create personalised challenges in the pupils' flow zone, between boredom and anxiety (Gallego-Durán et al, 2018). Feelings of competence and a sense of autonomy are important factors that generate variability in feelings of flow and intrinsic motivation.…”

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“…The paper by Gallego-Duran et al [35] deals with a very interesting problem, i.e., how to define activities for adaptive learning systems. Adaptive learning aims to maintain learners in a permanent state of flow by providing them the activities adapted to their skills.…”