Assessment as Engagement, Assessment as Outreach: Understanding Student Learning About Research at the Trinity College Library

This presentation was offered as part of the CUNY Library Assessment Conference, Reinventing Libraries: Reinventing Assessment, held at the City University of New York in June 2014.

Air Conditioning and Refrigeration Student Looking at Plans

A student from the New York Trade School in the Air Conditioning and Refrigeration Dept. looks at plans on top of a building. Black and white photograph contains some damage from adhesive and writing on the front.

Student at work with closed-circuit TV

A student in the Closed-circuit TV Dept. at the New York Trade School is shown working. Black and white photograph.

Closed-circuit Television Student Working

A student is show working with closed-circuit television at the New York Trade School. Black and white photograph.

Data Processing Student Working

A student from the Data Processing program at the New York Trade School is shown working. Black and white photograph with some edge damage due to writing in black along the top.

Professor and Student in Piano Crafts Department

Here a professor in the Piano Crafts Department at the New York Trade School is shown helping a student. Black and white photograph.

Welding Student at Work

A student in the Welding Department at the New York Trade School is pictured working. Notice the sparks that are being emitted from the equipment. Black and white photograph.

Sign Painting Student Working Outside

A Sign Painting student from the New York Trade School is pictured working outside on scaffolding on an AMOCO sign. Black and white photograph.

The Impact of the NIHERST/NGC National Science Centre, Trinidad and Tobago on Visiting Student Groups

The aim of this study was to asses the impact of a visit to the NIHERST/NGC National Science Centre in Trinidad on four different school-age visitor groups. The research was conducted through the administering of a post-visit questionnaire immediately upon completion of each visit by each group, and via visitor feedback obtained in post-visit or pre-visit activities conducted within two weeks of the visit for three groups. Teachers/instructors who accompanied the groups on their visit also completed post-visit questionnaires and provided additional information on follow-up activities via an interview. The results of this investigation suggest that the visit to this science centre provided entertainment/enjoyment value and potential educational value to most individuals. The nature of this enjoyment was noted for various age groups and genders in this study. Quantification of the educational impact was not possible within the constraints of this study, which was unable to capture long-term effects of the supply of ‘new knowledge’ to visitors which the visit to the science centre had provided.

Modelling Recreation Demand Using Choice Experiments : Using Swedish Snowmobilers Demand for Groomed trails

This paper is concerned with the use of the choice experiment method for modeling the demand for snowmobiling . The Choice Experiment includes five attributes, standard, composition, length, price day card and experience along trail. The paper estimates the snowmobile owners’ preferences and the most preferred attributes, including their will-ingness to pay for a daytrip on groomed snowmobile trail. The data consists of the an-swers from 479 registered snowmobile owners, who answered two hypothetical choice questions each. Estimating using the multinominal logit model, it is found that snow-mobilers on average are willing to pay 22.5 SEK for one day of snowmobiling on a trail with quality described as skidded every 14th day. Furthermore, it is found that the WTP increases with the quality of trail grooming. The result of this paper can be used as a yardstick for snowmobile clubs wanting to develop their trail net worth, organizations and companies developing snowmobiling as a recreational activities and marketers in-terested in marketing snowmobiling as recreational activities.

The Choice of STIGA Table Tennis Blades : Evidence from China

The purpose of this paper is to investigate how individuals with different characteristics make their choice-decisions when consuming STIGA table tennis blades, which are combinations of various attributes, such as price, control, attack, etc. It is expected that the general trend of choice behavior on this special commodity can be, at least to some extent, revealed. Data were collected using questionnaires sent to registered members of a table tennis club in China. The questionnaires included information and questions about individuals’ monthly income levels, ages, technique styles, etc. A multinomial logit model was then applied to analyze factors determining Chinese consumers’ choice behavior on STIGA table tennis blades. The results indicated that the main element influencing Chinese consumers’ choice of STIGA ping-pong blades was the technique style and other variables did not seem to influence the choice of table tennis blades. These results might be explained by the limited sample size as well as unmeasured and immeasurable factors. Thus, a more extensive research is needed to be conducted in the future.

ANFIS BASED MODELS FOR ACCESSING QUALITY OF WIKIPEDIA ARTICLES

Wikipedia is a free, web-based, collaborative, multilingual encyclopedia project supported by the non-profit Wikimedia Foundation. Due to the free nature of Wikipedia and allowing open access to everyone to edit articles the quality of articles may be affected. As all people don’t have equal level of knowledge and also different people have different opinions about a topic so there may be difference between the contributions made by different authors. To overcome this situation it is very important to classify the articles so that the articles of good quality can be separated from the poor quality articles and should be removed from the database. The aim of this study is to classify the articles of Wikipedia into two classes class 0 (poor quality) and class 1(good quality) using the Adaptive Neuro Fuzzy Inference System (ANFIS) and data mining techniques. Two ANFIS are built using the Fuzzy Logic Toolbox [1] available in Matlab. The first ANFIS is based on the rules obtained from J48 classifier in WEKA while the other one was built by using the expert’s knowledge. The data used for this research work contains 226 article’s records taken from the German version of Wikipedia. The dataset consists of 19 inputs and one output. The data was preprocessed to remove any similar attributes. The input variables are related to the editors, contributors, length of articles and the lifecycle of articles. In the end analysis of different methods implemented in this research is made to analyze the performance of each classification method used.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.