An evaluation of a curriculum response to the State of Florida mandate for computer literacy at a large comprehensive high school in Dade County, Florida

Minimum Student Performance Standards in Computer Literacy and Science were passed by the Florida Legislature through the Educational Reform Act of 1983. This act mandated that all Florida high school graduates receive training in computer literacy. Schools and school systems were charged with the task of determining the best methods to deliver this instruction to their students. The scope of this study is to evaluate one school's response to the state of Florida's computer literacy mandate. The study was conducted at Miami Palmetto Senior High School, located in Dade County, Florida. The administration of Miami Palmetto Senior High School chose to develop and implement a new program to comply with the state mandate - integrating computer literacy into the existing biology curriculum. The study evaluated the curriculum to determine if computer literacy could be integrated successfully and meet both the biology and computer literacy objectives. The findings in this study showed that there were no significant differences between biology scores of the students taking the integrated curriculum and those taking a traditional curriculum of biology. Student in the integrated curriculum not only met the biology objectives as well as those in the traditional curriculum, they also successfully completed the intended objectives for computer literacy. Two sets of objectives were successfully completed in the integrated classes in the same amount of time used to complete one set of objectives in the traditional biology classes. Therefore, integrated curriculum was the more efficient means of meeting the intended objectives of both biology and computer literacy.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.