Fast dynamic difficulty adjustment for intelligent tutoring systems with small datasets

This paper studies the problem of automatically adjusting the difficulty level of educational exercises to facilitate learning. Previous work on this topic either relies on large datasets or requires multiple interactions before it adjusts properly. Although this is sufficient for large-scale online courses, there are also scenarios where students are expected to only work through a few trials. In these cases, the adjustment needs to respond to only a few data points. To accommodate this, we propose a novel difficulty adjustment method that requires less data and adapts faster. Our proposed method refits an existing item response theory model to work on smaller datasets by generalizing based on attributes of the exercises. To adapt faster, we additionally introduce a discount value that weakens the influence of past interactions. We evaluate our proposed method on simulations and a user study using an example graph theory lecture. Our results show that our approach indeed succeeds in adjusting to learners quickly

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Knowledge Tracing: A Review of Available Technologies

As a student modeling technique, knowledge tracing is widely used by various intelligent tutoring systems to infer and trace the individual’s knowledge state during the learning process. In recent years, various models were proposed to get accurate and easy-to-interpret results. To make sense of the wide Knowledge tracing (KT) modeling landscape, this paper conducts a systematic review to provide a detailed and nuanced discussion of relevant KT techniques from the perspective of assumptions, data, and algorithms. The results show that most existing KT models consider only a fragment of the assumptions that relate to the knowledge components within items and student’s cognitive process. Almost all types of KT models take “quize data” as input, although it is insufficient to reflect a clear picture of students’ learning process. Dynamic Bayesian network, logistic regression and deep learning are the main algorithms used by various knowledge tracing models. Some open issues are identified based on the analytics of the reviewed works and discussed potential future research directions

Theory and Practice: Improving Retention Performance through Student Modeling and System Building

The goal of Intelligent Tutoring systems (ITSs) is to engage the students in sustained reasoning activity and to interact with students based on a deep understanding of student behavior. In order to understand student behavior, ITSs rely on student modeling methods to observes student actions in the tutor and creates a quantitative representation of student knowledge, interests, affective states. Good student models are going to effectively help ITSs customize instructions, engage student\u27s interest and then promote learning. Thus, the work of building ITSs and advancing student modeling should be considered as two interconnected components of one system rather than two separate topics. In this work, we utilized the theoretical support of a well-known learning science theory, the spacing effect, to guide the development of an ITS, called Automatic Reassessment and Relearning System (ARRS). ARRS not only validated the effectiveness of spacing effect, but it also served as a testing field which allowed us to find out new approaches to improve student learning by conducting large-scale randomized controlled trials (RCTs). The rich data set we gathered from ARRS has advanced our understanding of robust learning and helped us build student models with advanced data mining methods. At the end, we designed a set of API that supports the development of ARRS in next generation ASSISTments platform and adopted deep learning algorithms to further improve retention performance prediction. We believe our work is a successful example of combining theory and practice to advance science and address real- world problems

Overcoming foreign language anxiety in an emotionally intelligent tutoring system

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning. Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct and less regulated by the learner, but it is uncomfortable and requires the learner to be in the lab. Employing self-reports is scalable because it is easy to administer in the lab and online. However, it interrupts the learning flow, and people sometimes respond inaccurately. Using sensor-free human behavioral metrics is a scalable and practical measurement because it is feasible online or in class with minimum adjustments. To overcome FLA, researchers have studied the use of robots, games, or intelligent tutoring systems (ITS). Within these technologies, they applied soothing music, difficulty reduction, or storytelling. These methods lessened FLA but had limitations such as distracting the learner, not improving performance, and producing cognitive overload. Using an animated agent that provides motivational supportive feedback could reduce FLA and increase learning. It is necessary to measure FLA effectively with minimal interruption and then successfully reduce it. In the context of an e-learning system, I investigated ways to detect FLA using sensor-free human behavioral metrics. This scalable and practical method allows us to recognize FLA without being obtrusive. To reduce FLA, I studied applying emotionally adaptive feedback that offers motivational supportive feedback by an animated agent