Content and Timing of Feedback in a Web-based Learning Environment::Effects on Learning as a function of prior knowledge

This study investigated the effectiveness of different types of feedback content (elaborate vs. global) and feedback timing (immediate vs. delayed) for learning about genetics in a webbased learning environment as a function of learners’ prior knowledge. It was hypothesized that learning outcomes of students with low prior knowledge would be fostered by immediate elaborate feedback, whereas those of students with more prior knowledge would be enhanced by delayed global feedback. Students’ perceptions of the feedback they received were explored. Results showed a significant positive effect of global feedback on learning outcomes for higher prior knowledge learners, although those who received elaborate feedback gave a higher appreciation rating. The findings are discussed in terms of implications for the design and delivery of feedback in web-based learning environments

Lattice-Based Approach to Building Templates for Natural Language Understanding in Intelligent Tutoring Systems

We describe a domain-independent authoring tool, ConceptGrid, that helps non-programmers develop intelligent tutoring systems (ITSs) that perform natural language processing. The approach involves the use of a lattice-style table-driven interface to build templates that describe a set of required concepts that are meant to be a part of a student’s response to a question, and a set of incorrect concepts that reflect incorrect understanding by the student. The tool also helps provide customized just-in-time feedback based on the concepts present or absent in the student’s response. This tool has been integrated and tested with a browser-based ITS authoring tool called xPST

Uncovering cognitive processes: Different techniques that can contribute to cognitive load research and instruction

This article discusses the use of different techniques for uncovering cognitive processes, for research and instructional purposes: verbal reporting, eye tracking, and concept mapping. It is argued here that applying these techniques in research inspired by Cognitive Load Theory may increase our understanding of how and why well-known effects of instructional formats come about (e.g., split-attention, redundancy, or worked example effects) and refine or corroborate the proposed theoretical underpinnings of such effects. This knowledge can inform instructional design, and moreover, the effects of these techniques on learning can also be direct, by embedding the techniques in instruction

Using ConceptGrid as an easy authoring technique to check natural language responses

ConceptGrid provides a template-style approach to check natural language responses by students using a model-tracing style intelligent tutoring system. The tutor-author creates, using a web-based authoring system, a latticestyle structure that contains the set of required concepts that need to be in a student response. The author can also create just-in-time feedback based on the concepts present or absent in the student\u27s response. ConceptGrid is integrated within the xPST authoring tool and was tested in two experiments, both of which show the efficacy of the technique to check student answers. The first study tested the tutor\u27s effectiveness overall in the domain of statistics. The second study investigated ConceptGrid\u27s use by non-programmers and non-cognitive scientists. ConceptGrid extends existing capabilities for authoring of intelligent tutors by using this template-based approach for checking sentence-length natural language input

Intelligent Tutoring System Authoring Tools for Non-Programmers

An intelligent tutoring system (ITS) is a software application that tries to replicate the performance of a human tutor by supporting the theory of learning by doing . ITSs have been shown to improve the performance of a student in wide range of domains. Despite their benefits, ITSs have not seen widespread use due to the complexity involved in their development. Developing an ITS from scratch requires expertise in several fields including computer science, cognitive psychology and artificial intelligence. In order to decrease the skill threshold required to build ITSs, several authoring tools have been developed. In this thesis, I document several contributions to the field of intelligent tutoring in the form of extensions to an existing ITS authoring tool, research studies on authoring tool paradigms and the design of authoring tools for non-programmers in two complex domains - natural language processing and 3D game environments. The Extensible Problem Specific Tutor (xPST) is an authoring tool that helps rapidly develop model-tracing like tutors on existing interfaces such as webpages. xPST\u27s language was made more expressive with the introduction of new checktypes required for answer checking in problems belonging to domains such as geometry and statistics. A web-based authoring (WAT) tool was developed for the purpose of tutor management and deployment and to promote non-programmer authoring of ITSs. The WAT was used in a comparison study between two authoring tool paradigms - GUI based and text based, in two different problem domains - statistics and geometry. User-programming of natural language processing (NLP) in ITSs is not common with authoring toolkits. Existing NLP techniques do not offer sufficient power to non-programmers and the NLP is left to expert developers or machine learning algorithms. We attempted to address this challenge by developing a domain-independent authoring tool, ConceptGrid that is intended to help non-programmers develop ITSs that perform natural language processing. ConceptGrid has been integrated into xPST. When templates created using ConceptGrid were tested, they approached the accuracy of human instructors in scoring student responses. 3D game environments belong to another domain for which authoring tools are uncommon. Authoring game-based tutors is challenging due to the inherent domain complexity and dynamic nature of the environment. We attempt to address this challenge through the design of authoring tool that is intended to help non-programmers develop game-based ITSs

Aplicación de modelado de tópicos en reseñas de hospedajes de Airbnb en Berlín de 2010 a 2019

El Procesamiento de Lenguaje Natural se ha convertido en una disciplina clave en la era digital. Con el aumento en la cantidad de información que se genera diariamente en diferentes formatos, es necesario contar con herramientas que permitan analizar, entender y categorizar los datos. Ante esta realidad se desarrolla este trabajo explorando una de estas herramientas, el modelado de tópicos. Este trabajo se divide en dos principales partes, en la primera se hace una comparativa de forma metodológica, de tres modelos (LDA, GSDMM y BERTopic) para el modelado de tópicos. Y en la segunda parte, se hace un análisis de tópicos con el modelo seleccionado de la primera parte. El trabajo se desarrolló con una base de datos de reseñas de alojamientos de Airbnb Berlín. Previa a las dos principales partes se realizó un pretratamiento de la base de datos, el cual incluye pasos como selección de columnas, detección de idioma de las reseñas, traducción al inglés, tokenizado, eliminación de stopwords y stemming. Posterior a esto se realizó la comparativa empleando las siguientes métricas, valor de coherencia, análisis visual y tiempo de ejecución. De la comparativa se seleccionó el modelo LDA, debido a que es el que presenta mejores resultados en todas las métricas. Enseguida realizo una optimización de los parámetros, haciendo un barrido de los valores posibles en un rango definido. Como resultado del modelo optimizado se encontraron cinco tópicos principales en el conjunto de reseñas, los cuales se categorizan como “Positivo General”, “Negativo General”, “Ubicación Airbnb”, “Positivo Host” y “Habitaciones/Espacio del Airbnb”. Siendo “Positivo General” el tópico principal en el conjunto y el tópico “Habitaciones/Espacio del Airbnb” muestra mayor relevancia en reseñas escritas en español y francés

How Domain Differences Impact the Mode Structure of Expert Tutoring Dialogue

Whitney Layne Cade granted permission for the digitization of her paper. It was submitted by CDWhile human-to-human dialogue in tutoring sessions has received considerable attention in the last 25 years, there exists a paucity of work examining the pedagogical and motivational strategies of expert human tutors. An established trend in the tutorial dialogue community is to study tutorial dialogues in a very fine-grained manner, at the level of the speech act or dialogue move. The present work offers a coding scheme that examines larger, pedagogically distinct phases as the unit of analysis, referred to as “modes”, which exist in expert tutoring and provide the context needed to understand patterns of dialogue moves. The eight modes identified by this coding scheme are the Introduction, Lecture, Modeling, Scaffolding, Fading, Highlighting, Off Topic, and Conclusion mode, and each mode was reliably identified at or above the .8 kappa level. After determining how often modes occur and the amount of dialogue devoted to them in expert tutoring sessions, differences between the domains of math and science were investigated. Significant variance between the domains was revealed using this largergrained coding scheme, particularly in how Lecture and Scaffolding are used in expert tutoring. While these two modes tend to dominate most tutorial dialogue in this sample regardless of domain, the differences in their frequency and the amount of dialogue devoted to each mode suggest diverse tutoring goals associated with each domain. Other subtle differences in mode distributions draw attention both to the complexities of expert tutoring and the danger of generalizing tutorial structures across domains.This honors paper was approved by Dr. Natalie Person, Dr. Chris Wetzel, and Dr. Andrew Olne

Supporting the tutor in the design and support of adaptive e-learning

The further development and deployment of e-learning faces a number of threats. First, in order to meet the increasing demands of learners, staff have to develop and plan a wide and complex variety of learning activities that, in line with contemporary pedagogical models, adapt to the learners’ individual needs. Second, the deployment of e-learning, and therewith the freedom to design the appropriate kind of activities is bound by strict economical conditions, i.e. the amount of time available to staff to support the learning process. In this thesis two models have been developed and implemented that each address a different need. The first model covers the need to support the design task of staff, the second one the need to support the staff in supervising and giving guidance to students' learning activities. More specifically, the first model alleviates the design task by offering a set of connected design and runtime tools that facilitate adaptive e-learning. The second model alleviates the support task by invoking the knowledge and skills of fellow-students. Both models have been validated in near-real-world task settings