As educators turn to technology to supplement classroom instruction, the integration of natural language processing (NLP) into educational technologies is vital for increasing student success. NLP involves the use of computers to analyze and respond to human language, including students' responses to a variety of assignments and tasks. While NLP…

This work proposes Dynamic Linear Epsilon-Greedy, a novel contextual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning approaches have trade-offs between empirical investigation and maximal impact on users. Our…

To meet the goal of understanding students' complex learning processes and maximizing their learning outcomes, the field of learning analytics delves into the myriad of data captured as students use computer assisted learning platforms. Although many platforms associated with learning analytics focus on students' performance, performance on…

For researchers seeking to improve education, a common goal is to identify teaching practices that have causal benefits in classroom settings. To test whether an instructional practice exerts a causal influence on an outcome measure, the most straightforward and compelling method is to conduct an experiment. While experimentation is common in…

Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…

Online educational games have been widely used to support students' mathematics learning. However, their effects largely depend on student-related factors, the most prominent being their behavioral characteristics as they play the games. In this study, we applied a set of learning analytics methods ("k"-means clustering, data…

This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction; and…

This paper demonstrates how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. We examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance prediction; and (2) what types of in-game features were associated with student…

Accurately modeling individual students' knowledge growth is important in many applications of learning analytics. A key step is to decompose the knowledge targeted in the instruction into detailed knowledge components (KCs). We search for an accurate KC model for basic equation solving skills, using data from an intelligent tutoring system (ITS),…

In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…

Purpose: Most educational assessments tend to be constructed in a close-ended format, which is easier to score consistently and more affordable. However, recent work has leveraged computation text methods from the information sciences to make open-ended measurement more effective and reliable for older students. This study asks whether such text…

Classroom experiments that evaluate the effectiveness of educational technologies do not typically examine the effects of classroom contextual variables (e.g., out-of-software help-giving and external distractions). Yet these variables may influence students' instructional outcomes. In this paper, we introduce the Spatial Classroom Log Explorer…

Students conducted inquiry using simulations within a rich learning environment for 4 science topics. By applying educational data mining to students' log data, assessment metrics were generated for two key inquiry skills, testing stated hypotheses and designing controlled experiments. Three models were then developed to analyze the transfer of…

In recent years, an increasing number of analyses in Learning Analytics and Educational Data Mining (EDM) have adopted a "Discovery with Models" approach, where an existing model is used as a key component in a new EDM/analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its…