Improving academic learning from computer-based narrative games

Although many strong claims are made for the power of computer games to promote academic learning, the narrative content of a game may reduce the learner's tendency to reflect on its academic content. The present study examines adding a low-cost instructional feature intended to promote appropriate cognitive processing of the academic content during play. College students played a computer adventure game in which they guided a character through a bunker in search of lost artwork, building electromechanical devices to open stuck doors along the way. In Experiment 1, students who filled out worksheets about wet-cell batteries before and during the game outperformed students who played the game without worksheets on a written explanation of how wet-cell batteries work (d = 0.92), multiple-choice comprehension questions about wet-cell batteries (d = 0.67), and open-ended transfer problems about wet-cell batteries (d = 0.74). In Experiment 2, participants who completed only the in-game worksheet outperformed the control group on a written explanation of wet-cell batteries (d = 0.59) and transfer problems (d = 0.67), whereas participants who completed only the pre-game worksheet did not outperform the control group on any measure. These findings point to the learning benefits of adding instructional features suggested by cognitive theories of learning

Autophagy: research topic, painting, poem, dance…

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111807/1/embr201540400.pd

Assessing the Role of Computer Simulation in Chemistry Learning

Simulation and Computation make a versatile teaching strategy, and may be an important way to motivate students and lecturers to achieve meaningful learning. Indeed, this work refers to a study whose main objective is to set the influence that a teaching approach based on the use of computer simulation would have on students’ learning, compared to the one in use today. This work involved the participation of two classes of 11th grade at a Secondary School in Lisbon, Portugal, where the main goal is to teach a specific topic to an untried student’s group. With regard to the simulation environment, it will be grounded on a Proof Theoretical approach to Knowledge Representation and Reasoning, which caters for the handling of incomplete, unknown or even self-contradictory information or knowledge

Learner control in animated multimedia instructions

The interactivity principle in multimedia learning states that giving learners control over pace and order of instructions decreases cognitive load and increases transfer performance. We tested this guideline by comparing a learner-paced instruction with a system-paced instruction. Time-on-task and interactive behavior were logged, and were also related to interest, prior knowledge, and cognitive involvement. We successfully replicated the interactivity principle in terms of better transfer. However, this coincided with a large increase in time-on-task. Also, large individual differences existed in the use of learner control options, which were mostly unrelated to the other variables. Thus, the benefits of introducing learner control in multimedia learning are at the expense of learning efficiency, and it remains unclear for whom the interactivity principle works best

A Theoretical Analysis of How Segmentation of Dynamic Visualizations Optimizes Students' Learning

This article reviews studies investigating segmentation of dynamic visualizations (i.e., showing dynamic visualizations in pieces with pauses in between) and discusses two not mutually exclusive processes that might underlie the effectiveness of segmentation. First, cognitive activities needed for dealing with the transience of dynamic visualizations impose extraneous cognitive load, which may hinder learning. Segmentation may reduce the negative effect of this load by dividing animations into smaller units of information and providing pauses between segments that give students time for the necessary cognitive activities after each of those units of information. Second, event segmentation theory states that people mentally segment dynamic visualizations during perception (i.e., divide the information shown in pieces). Segmentation of dynamic visualisation could cue relevant segments to students, which may aid them in perceiving the structure underlying the process or procedure shown

Second chances: Investigating athletes’ experiences of talent transfer

Talent transfer initiatives seek to transfer talented, mature individuals from one sport to another. Unfortunately talent transfer initiatives seem to lack an evidence-based direction and a rigorous exploration of the mechanisms underpinning the approach. The purpose of this exploratory study was to identify the factors which successfully transferring athletes cite as facilitative of talent transfer. In contrast to the anthropometric and performance variables that underpin current talent transfer initiatives, participants identified a range of psychobehavioral and environmental factors as key to successful transfer. We argue that further research into the mechanisms of talent transfer is needed in order to provide a strong evidence base for the methodologies employed in these initiatives

Which Distributions (or Families of Distributions) Best Represent Interval Uncertainty: Case of Permutation-Invariant Criteria

In many practical situations, we only know the interval containing the quantity of interest, we have no information about the probability of different values within this interval. In contrast to the cases when we know the distributions and can thus use Monte-Carlo simulations, processing such interval uncertainty is difficult -- crudely speaking, because we need to try all possible distributions on this interval. Sometimes, the problem can be simplified: namely, it is possible to select a single distribution (or a small family of distributions) whose analysis provides a good understanding of the situation. The most known case is when we use the Maximum Entropy approach and get the uniform distribution on the interval. Interesting, sensitivity analysis -- which has completely different objectives -- leads to selection of the same uniform distribution. In this paper, we provide a general explanation of why uniform distribution appears in different situations -- namely, it appears every time we have a permutation-invariant objective functions with the unique optimum. We also discuss what happens if there are several optima