Cyberlearning presents numerous challenges such as the lack of personal and assessment-driven learning, how students are often puzzled by the lack of instructor guidance and feedback, the huge volume of diverse learning materials, and the inability to zoom in from the general concepts to the more specific ones, or vice versa. Intelligent tutoring…

Research on collaborative learning between humans and virtual pedagogical agents represents a necessary extension to recent research on the conceptual, theoretical, methodological, analytical, and educational issues behind co- and socially-shared regulated learning between humans. This study presents a novel coding framework that was developed and…

Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically,…

Games with educational purposes usually follow a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problem, some ideas from Intelligent Tutoring Systems have been used in educational games such as teaching introductory programming. The objective of this study was to…

Latent Semantic Analysis (LSA) plays an important role in analyzing text data from education settings. LSA represents meaning of words and sets of words by vectors from a k-dimensional space generated from a selected corpus. While the impact of the value of k has been investigated by many researchers, the impact of the selection of documents and…

Randomized A/B tests in educational software are not run in a vacuum: often, reams of historical data are available alongside the data from a randomized trial. This paper proposes a method to use this historical data--often highdimensional and longitudinal--to improve causal estimates from A/B tests. The method proceeds in two steps: first, fit a…

Interactive Strategy Training for Active Reading and Thinking en Español, or iSTART-E, is a new intelligent tutoring system (ITS) that provides reading comprehension strategy training for Spanish speakers. This paper reports on studies evaluating the efficacy of iSTART-E in real-world classrooms in two different Spanish-speaking countries. In…

Students with their different learning styles also have their own different learning approaches, and teachers cannot simultaneously facilitate them all. Teachers' limitation in serving all students' learning styles can be anticipated by the use of computer-based instructions. This research aims to develop ScEd-Adaptive Learning System (ScEd-ASL)…

Estimating the prerequisite structure of skills is a crucial issue in domain modeling. Students usually learn skills in sequence since the preliminary skills need to be learned prior to the complex skills. The prerequisite relations between skills underlie the design of learning sequence and adaptation strategies for tutoring systems. The…

This article introduces a special issue comprising research on efforts to personalize learning in different academic subjects. We first consider the emergence of personalized learning (PL) and the myriad of definitions that describe its essential features. Thereafter, we introduce the articles in the special issue by examining their alignment to…

Purpose: Explore how different automated scoring (AS) models score reliably the expressive language and vocabulary knowledge in depth of young second grade Latino English learners. Design/methodology/approach: Analyze a total of 13,471 English utterances from 217 Latino English learners with random forest, end-to-end memory networks, long…

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…

Influenced by COVID-19, online learning has become one of the most important forms of education in the world. In the era of intelligent education, knowledge tracing (KT) can provide excellent technical support for individualized teaching. For online learning, we come up with a new knowledge tracing method that integrates mathematical exercise…

Adaptive curriculum sequencing (ACS) is still a challenge in the adaptive learning field. ACS is a NP-hard problem especially considering the several constraints of the student and the learning material when selecting a sequence from repositories where several sequences could be chosen. Therefore, this has stimulated several researchers to use…

As artificial intelligence (AI) increasingly enters K-12 classrooms, what do teachers and students see as the roles of human versus AI instruction, and how might educational AI (AIED) systems best be designed to support these complementary roles? We explore these questions through participatory design and needs validation studies with K12 teachers…