A compensatory model for simultaneously setting cutting scores for selection-placement-mastery decisions

A method is proposed for optimizing cutting scores for a selection-placement-mastery problem simultaneously. A simultaneous approach has two advantages over separate optimization. First, test scores used in previous decisions can be used as "prior data" in later decisions, increasing the efficiency of the decisions. Then, more realistic utility structures can be defined using final success criteria in utility functions for earlier decisions. An important distinction is made between weak and strong decision rules. Weak rules are allowed to be a function of prior test scores. Conditions for optimal rules to be monotone are presented, and it is shown that optimal weak monotone rules are compensatory by nature. Results from an empirical example of instructional decision making illustrate the differences between simultaneous and separate approaches. Subjects were 71 medical students receiving interactive video or computer-aided instruction

The Impact of Task Difficulty on Reading Comprehension Intervention with Computer Agents

The Impact of Task Difficulty on Reading Comprehension Intervention with Computer Agent

A semantic memory bank assisted by an embodied conversational agents for mobile devices

Alzheimer’s disease is a type of dementia that causes memory loss and interferes with intellectual abilities seriously. It has no current cure and therapeutic efficiency of current medication is limited. However, there is evidence that non-pharmacological treatments could be useful to stimulate cognitive abilities. In the last few year, several studies have focused on describing and under- standing how Virtual Coaches (VC) could be key drivers for health promotion in home care settings. The use of VC gains an augmented attention in the considerations of medical innovations. In this paper, we propose an approach that exploits semantic technologies and Embodied Conversational Agents to help patients training cognitive abilities using mobile devices. In this work, semantic technologies are used to provide knowledge about the memory of a specific person, who exploits the structured data stored in a linked data repository and take advantage of the flexibility provided by ontologies to define search domains and expand the agent’s capabilities. Our Memory Bank Embodied Conversational Agent (MBECA) is used to interact with the patient and ease the interaction with new devices. The framework is oriented to Alzheimer’s patients, caregivers, and therapists

PROMPTING SELF-EXPLANATIONS DURING THE LEARNING OF PROBABILITY: CONTENT-SPECIFIC VERSUS GENERIC VERSUS GENERIC WITH A FORM OF GUIDANCE

PROMPTING SELF-EXPLANATIONS DURING THE LEARNING OF PROBABILITY: CONTENT-SPECIFIC VERSUS GENERIC VERSUS GENERIC WITH A FORM OF GUIDANC