Opening up the interpretation process in an open learner model

Opening a model of the learner is a potentially complex operation. There are many aspects of the learner that can be modelled, and many of these aspects may need to be opened in different ways. In addition, there may be complicated interactions between these aspects which raise questions both about the accuracy of the underlying model and the methods for representing a holistic view of the model. There can also be complex processes involved in inferring the learner's state, and opening up views onto these processes - which leads to the issues that are the main focus of this paper: namely, how can we open up the process of interpreting the learner's behaviour in such a manner that the learner can both understand the process and challenge the interpretation in a meaningful manner. The paper provides a description of the design and implementation of an open learner model (termed the xOLM) which features an approach to breaking free from the limitations of "black box" interpretation. This approach is based on a Toulmin-like argumentation structure together with a form of data fusion based on an adaptation of Dempster-Shafer. However, the approach is not without its problems. The paper ends with a discussion of the possible ways in which open learner models might open up the interpretation process even more effectively

Design of interactive visualization of models and students data

This document reports the design of the interactive visualizations of open student models that will be performed in GRAPPLE. The visualizations will be based on data stored in the domain model and student model, and aim at supporting learners to be more engaged in the learning process, and instructors in assisting the learners

Applying Academic Analytics: Developing a Process for Utilizing Bayesian Networks to Predict Stopping Out Among Community College Students

abstract: Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory and confirmatory techniques to reduce an initial pool of more than 50 potential predictor variables to a parsimonious final BN with only four predictor variables. The average in-sample classification accuracy rate for the model was 80% (Cohen's κ = 53%). The model was shown to be generalizable across samples with an average out-of-sample classification accuracy rate of 78% (Cohen's κ = 49%). The classification rates for the BN were also found to be superior to the classification rates produced by an analog frequentist discrete-time survival analysis model.Dissertation/ThesisDoctoral Dissertation Educational Psychology 201

Computational Psychometrics for Item-based Computerized Adaptive Learning

With advances in computer technology and expanded access to educational data, psychometrics faces new opportunities and challenges for enhancing pattern discovery and decision-making in testing and learning. In this dissertation, I introduced three computational psychometrics studies for solving the technical problems in item-based computerized adaptive learning (CAL) systems related to dynamic measurement, diagnosis, and recommendation based on Bayesian item response theory (IRT). For the first study, I introduced a new knowledge tracing (KT) model, dynamic IRT (DIRT), which can iteratively update the posterior distribution of latent ability based on moment match approximation and capture the uncertainty of ability change during the learning process. For dynamic measurement, DIRT has advantages in interpretation, flexibility, computation cost, and implementability. For the second study, A new measurement model, named multilevel and multidimensional item response theory with Q matrix (MMIRT-Q), was proposed to provide fine-grained diagnostic feedback. I introduced sequential Monte Carlo (SMC) for online estimation of latent abilities. For the third study, I proposed the maximum expected ratio of posterior variance reduction criterion (MERPV) for testing purposes and the maximum expected improvement in posterior mean (MEIPM) criterion for learning purposes under the unified framework of IRT. With these computational psychometrics solutions, we can improve the students’ learning and testing experience with accurate psychometrics measurement, timely diagnosis feedback, and efficient item selection

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data