The influence of student characteristics on the use of adaptive e-learning material

Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive elearning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting demographic data and measuring motivation and prior knowledge. We also measured the learning paths students followed and learning strategies they used when working with adaptive e-learning material in a molecular biology course. We then combined these data to study if and how student characteristics relate to the learning paths and strategies they used. We observed that students did follow different learning paths. Gender did not have an effect, but (mainly Dutch) BSc students differed from (international) MSc students in the intrinsic motivation they had and the learning paths and strategies they followed when using the adaptive e-learning materia

Users’ Help-Seeking Behaviors within the Context of Computer Task Accomplishment: An Exploratory Study

This study investigated computer users’ help-seeking behaviors within the context of accomplishing a novel and challenging computer task. In addition, this study examined how different help-seeking behavioral variables relate to both personal factors and outcome measures in an exploratory manner. Finally, a structural model examined the effect of personal factors on task performance through the mediating function of help-seeking effectiveness. A total of 67 undergraduate students participated in the study. Participants were asked to perform a challenging task in Microsoft Word. The usability software MORAE was used to record the interactions between participants and computer systems. Participants had access to five help sources: “F1 help,” “reference book,” “the Web,” “video tutorial,” and “lab assistant,” which differ in media type (electronic vs. non-electronic) and interactivity levels (high vs. low). This study found that participants showed a wide range of help-seeking behaviors. Some participants were more active in seeking help than others. Participants also engaged in different help-seeking patterns when using different help sources. A dominant help-seeking strategy was to stay with the same source used in the previous help-seeking episode. Help-seeking behavior affected task performances, but personal factors had no significant effect on help seeking or task performances. Based on the findings, the research value of this study, its practical implications, its limitations, and future research directions are discussed

Trait Arousability And Its Impact On Adaptive Multimedia Training

Today\u27s best intelligent, adaptive, multimedia trainers have shown excellent performance; however, their results still fall far-short of what good human tutors can achieve. The overarching thesis of this paper is that future intelligent, adaptive systems will be improved by taking into account relevant, consistent, and meaningful individual differences. Specifically, responding to individual differences among trainees will (a) form more accurate individual baselines within a training system, and (b) better inform system responses (so that they interpret and respond to observable data more appropriately). One variable to consider is trait arousability, which describes individual differences in sensitivity to stimuli. Individuals\u27 arousability interacts with the arousal inherent to a task/environment to create a person\u27s arousal state. An individual\u27s arousal state affects his/her attentional capacity, working memory function, and depth of processing. In this paper, two studies are presented. The purpose of the first study was to evaluate existing subjective measures of trait arousability and then develop a new measure by factor analyzing existing apparatus. From this well-populated (N = 622) study, a new reliable ([alpha] = .91) 35-item scale was developed. This scale includes two factors, negative emotionality and orienting sensitivity, which have been previously theorized but not yet so reliably measured. The purposes of the second study were to (a) validate the measure developed in the first investigation and (b) demonstrate the applied value of the arousability construct in the context of training. Results from the second study (N=45) demonstrated significant main effects, but the interaction effects were inconclusive. They neither clearly confirm nor invalidate the hypotheses, but they do raise further questions

Examining Cognitive Empathy Elements within AI Chatbots for Healthcare Systems

Empathy is an essential part of communication in healthcare. It is a multidimensional concept and the two key dimensions: emotional and cognitive empathy allow clinicians to understand a patient’s situation, reasoning, and feelings clearly (Mercer and Reynolds, 2002). As artificial intelligence (AI) is increasingly being used in healthcare for many routine tasks, accurate diagnoses, and complex treatment plans, it is becoming more crucial to incorporate clinical empathy into patient-faced AI systems. Unless patients perceive that the AI is understanding their situation, the communication between patient and AI may not sustain efficiently. AI may not really exhibit any emotional empathy at present, but it has the capability to exhibit cognitive empathy by communicating how it can understand patients’ reasoning, perspectives, and point of view. In my dissertation, I examine this issue across three separate lab experiments and one interview study. At first, I developed AI Cognitive Empathy Scale (AICES) and tested all empathy (emotional and cognitive) components together in a simulated scenario against control for patient-AI interaction for diagnosis purposes. In the second experiment, I tested the empathy components separately against control in different simulated scenarios. I identified six cognitive empathy elements from the interview study with first-time mothers, two of these elements were unique from the past literature. In the final lab experiment, I tested different cognitive empathy components separately based on the results from the interview study in simulated scenarios to examine which element emerges as the most effective. Finally, I developed a conceptual model of cognitive empathy for patient-AI interaction connecting the past literature and the observations from my studies. Overall, cognitive empathy elements show promise to create a shared understanding in patients-AI communication that may lead to increased patient satisfaction and willingness to use AI systems for initial diagnosis purposes

A conceptual model of personalized virtual learning environments

The Virtual Learning Environment (VLE) is one of the fastest growing areas in educational technology research and development. In order to achieve learning effectiveness, ideal VLEs should be able to identify learning needs and customize solutions, with or without an instructor to supplement instruction. They are called Personalized VLEs (PVLEs). In order to achieve PVLEs success, comprehensive conceptual models corresponding to PVLEs are essential. Such conceptual modeling development is important because it facilitates early detection and correction of system development errors. Therefore, in order to capture the PVLEs knowledge explicitly, this paper focuses on the development of conceptual models for PVLEs, including models of knowledge primitives in terms of learner, curriculum, and situational models, models of VLEs in general pedagogical bases, and particularly, the definition of the ontology of PVLEs on the constructivist pedagogical principle. Based on those comprehensive conceptual models, a prototyped multiagent-based PVLE has been implemented. A field experiment was conducted to investigate the learning achievements by comparing personalized and non-personalized systems. The result indicates that the PVLE we developed under our comprehensive ontology successfully provides significant learning achievements. These comprehensive models also provide a solid knowledge representation framework for PVLEs development practice, guiding the analysis, design, and development of PVLEs. (c) 2005 Elsevier Ltd. All rights reserved

Detecting students who are conducting inquiry Without Thinking Fastidiously (WTF) in the Context of Microworld Learning Environments

In recent years, there has been increased interest and research on identifying the various ways that students can deviate from expected or desired patterns while using educational software. This includes research on gaming the system, player transformation, haphazard inquiry, and failure to use key features of the learning system. Detection of these sorts of behaviors has helped researchers to better understand these behaviors, thus allowing software designers to develop interventions that can remediate them and/or reduce their negative impacts on student learning. This work addresses two types of student disengagement: carelessness and a behavior we term WTF (“Without Thinking Fastidiously”) behavior. Carelessness is defined as not demonstrating a skill despite knowing it; we measured carelessness using a machine learned model. In WTF behavior, the student is interacting with the software, but their actions appear to have no relationship to the intended learning task. We discuss the detector development process, validate the detectors with human labels of the behavior, and discuss implications for understanding how and why students conduct inquiry without thinking fastidiously while learning in science inquiry microworlds. Following this work we explore the relationship between student learner characteristics and the aforementioned disengaged behaviors carelessness and WTF. Our goal was to develop a deeper understanding of which learner characteristics correlate to carelessness or WTF behavior. Our work examines three alternative methods for predicting carelessness and WTF behaviors from learner characteristics: simple correlations, k-means clustering, and decision tree rule learners