The Effect of using online homework exercises on student achievement in a Quantitative methods course

Abstract: Quantitative Methods (QM) is a compulsory course in the Social Science program in CEGEP. Many QM instructors assign a number of homework exercises to give students the opportunity to practice the statistical methods, which enhances their learning. However, traditional written exercises have two significant disadvantages. The first is that the feedback process is often very slow. The second disadvantage is that written exercises can generate a large amount of correcting for the instructor. WeBWorK is an open-source system that allows instructors to write exercises which students answer online. Although originally designed to write exercises for math and science students, WeBWorK programming allows for the creation of a variety of questions which can be used in the Quantitative Methods course. Because many statistical exercises generate objective and quantitative answers, the system is able to instantly assess students’ responses and tell them whether they are right or wrong. This immediate feedback has been shown to be theoretically conducive to positive learning outcomes. In addition, the system can be set up to allow students to re-try the problem if they got it wrong. This has benefits both in terms of student motivation and reinforcing learning. Through the use of a quasi-experiment, this research project measured and analysed the effects of using WeBWorK exercises in the Quantitative Methods course at Vanier College. Three specific research questions were addressed. First, we looked at whether students who did the WeBWorK exercises got better grades than students who did written exercises. Second, we looked at whether students who completed more of the WeBWorK exercises got better grades than students who completed fewer of the WeBWorK exercises. Finally, we used a self-report survey to find out what students’ perceptions and opinions were of the WeBWorK and the written exercises. For the first research question, a crossover design was used in order to compare whether the group that did WeBWorK problems during one unit would score significantly higher on that unit test than the other group that did the written problems. We found no significant difference in grades between students who did the WeBWorK exercises and students who did the written exercises. The second research question looked at whether students who completed more of the WeBWorK exercises would get significantly higher grades than students who completed fewer of the WeBWorK exercises. The straight-line relationship between number of WeBWorK exercises completed and grades was positive in both groups. However, the correlation coefficients for these two variables showed no real pattern. Our third research question was investigated by using a survey to elicit students’ perceptions and opinions regarding the WeBWorK and written exercises. Students reported no difference in the amount of effort put into completing each type of exercise. Students were also asked to rate each type of exercise along six dimensions and a composite score was calculated. Overall, students gave a significantly higher score to the written exercises, and reported that they found the written exercises were better for understanding the basic statistical concepts and for learning the basic statistical methods. However, when presented with the choice of having only written or only WeBWorK exercises, slightly more students preferred or strongly preferred having only WeBWorK exercises. The results of this research suggest that the advantages of using WeBWorK to teach Quantitative Methods are variable. The WeBWorK system offers immediate feedback, which often seems to motivate students to try again if they do not have the correct answer. However, this does not necessarily translate into better performance on the written tests and on the final exam. What has been learned is that the WeBWorK system can be used by interested instructors to enhance student learning in the Quantitative Methods course. Further research may examine more specifically how this system can be used more effectively.

THE EFFECT OF SCHOOL CLIMATE (STUDENT AND TEACHER ENGAGEMENT) ON STUDENT PERFORMANCE

Title of Dissertation: THE EFFECT OF SCHOOL CLIMATE (STUDENT AND TEACHER ENGAGEMENT) ON STUDENT PERFORMANCE Kenneth L. Marcus, Doctor of Education, 2016 Directed By: Dr. Thomas Davis, Assistant Professor, Education Policy and Leadership, Department of Teaching and Learning, Policy and Leadership This quantitative research study was designed to compute correlations/relationships of student engagement and student achievement of fifth grade students. Secondary information was collected on the relationship of FARMS, type of school, hope, and well-being on student achievement. School leaders are charged with ensuring that students achieve academically and demonstrate their ability by meeting identified targets on state and district mandated assessments. Due to increased pressure to meet targets, principals implement academic interventions to improve student learning and overlook the benefits of a positive school climate. This study has provided information on the impact of school climate on student achievement. To conduct this study, the researcher collected two sets of public fifth grade data (Gallup Survey student engagement scores and DSA reading, mathematics, and science scores) to determine the relationship of student performance and school climate. Secondary data were also collected on teacher engagement and the percentage of students receiving FARMS to determine the effect on students. The findings from this study reinforced the belief that school climate can have a positive effect on student achievement. This study contributed quantitative data about the relationship between school climate and school achievement.

Perceived learning environments and metacognitive strategy knowledge at the upper secondary school level

There is evidence that students benefit from teachers’ explicit fostering of metacognitive strategy knowledge (MSK). However, there is insufficient understanding about the effect of implicit promotion of MSK in regular school instruction. This study investigates the relationship between perceived characteristics of learning environments (social climate, support, autonomy, self-reflection) and students’ MSK. A representative cohort of students (Nt1 = 1,272/Nt2 = 1,126) in Grades 10 and 11 at schools at the upper secondary education level (ISCED Level 3A) in Switzerland participated in this two-wave longitudinal study. Multilevel analysis showed effects on both the individual and the class level. Students who experienced higher social integration showed a higher extent of MSK at the beginning of the school year than students who experienced less social integration. Perceived autonomy was also positively related to students’ MSK on the individual level. In contrast, the results showed a negative relationship between perceived self-reflection and students’ MSK. On the class level, there was a negative relationship between self-reflection and students’ MSK. Teachers’ support did not correlate with students’ MSK on either the individual or the class level. Implications of these results for education and further studies are discussed. (DIPF/Orig.)

The interaction of economic and pedagogical ideals in the context of workplace learning in Germany: a framework for empirical research – inspired by business ethics

As an effect of marketisation, the importance of workplace learning in Germany has increased. The article follows up on the long-standing discourse around the question of how economic and pedagogical ideals interact in this context. In order to develop a theoretical framework for empirical research, three major positions of the discipline of business ethics are introduced. Business ethics in more abstract ways deals with the very same question, namely how do ideas such as profit orientation interact with other norms and values? The new perspectives show that the discourse has been hitherto based on a specific understanding of economy. In order to derive an empirical answer to the research question, the question is re-formulated as follows: Which values are inherent in the decisions taken? Consequently, it suggests using the concept of ‘rationalities of justification’ for empirical research. The article shows how this concept can be applied by conducting a test run. (DIPF/Orig.)

Learning English is Fun! Incidental vocabulary acquisition of music and traditional class ninth graders via listening to pop songs

Finnish youth are constantly exposed to music and lyrics in English in their free time. It is likely that this has a positive effect on vocabulary learning. Learning vocabulary while simultaneously accompanied with melodies is likely to result in better learning outcomes. The present thesis covers a study on the vocabulary learning of traditional and music class ninth graders in a south-western upper comprehensive school in Finland, mainly concentrating on vocabulary learning as a by-product of listening to pop music and learning vocabulary through semantic priming. The theoretical background presents viable linguistic arguments and theories, which provide clarity for why it would be possible to learn English vocabulary via listening to pop songs. There is conflicting evidence on the benefits of music on vocabulary learning, and this thesis sets out to shed light on the situation. Additionally, incorporating pop music in English classes could assist in decreasing the gap between real world English and school English. The thesis is a mixed method research study consisting of both quantitative and qualitative research materials. The methodology comprises vocabulary tests both before and after pop music samples and a background questionnaire filled by students. According to the results, all students reported liking listening to music and they clearly listened to English pop music the most. A statistically significant difference was found when analysing the results of the differences in pre- and post-vocabulary tests. However, the traditional class appeared to listen to mainstream pop music more than the students in the music class, and thus it seems likely that the traditional class benefited more from vocabulary learning occurring via listening to pop songs. In conclusion, it can be established that it is possible to learn English vocabulary via listening to pop songs and that students wish their English lectures would involve more music-related vocabulary exercises in the future. Thus, when it comes to school learning, pop songs should be utilised in vocabulary learning, which could also in turn result in more diverse learning and the students could, more easily than before, relate to the themes and topics of the lectures. Furthermore, with the help of pop songs it would be possible to decrease the gap between school English and real-world English.

Automatic learning of pre-miRNAs from different species.

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. Conclusion: In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools.

THE EFFECT OF EASTERN KENTUCKY COAL FIRES ON LOCAL AIR QUALITY AND PERSONAL HEALTH

The purpose of this project was to investigate student learning in the areas of earth science and environmental responsibility using the subject of coal fires. Eastern Kentucky, where this study was performed, has several coal fires burning that affect the local air quality and may also affect the health of people living near them. This study was conducted during the regular education of 9th grade Earth Science classroom in Russell Independent Schools, located in Russell, Kentucky. Students conducted internet research, read current articles on the subject of coal fire emissions and effect on local ecology, and demonstrated what they learned through summative assessments. There were several aspects of coalmines and coal fires that students studied. Students were able to take this knowledge and information and use it as a learning tool to gain a better understanding of their own environment. Using the local history and geology of coalmines, along with the long tradition of mine production, was a very beneficial starting point, allowing students to learn about environmental impact, stewardship of their local environment, and methods of preserving and protecting the ecosystem.

A virtual agent to sustaining children's engagement in language learning

In this thesis we aimed to explore the potential of gamification - defined as “the use of game elements in non-game contexts” [30] - in increasing children's (aged 5 to 6) engagement with the task. This is mainly due to the fact that our world is living a technological era, and videogames are an example of this engagement by being able to maintain children’s (and adults) engagement for hours straight. For the purpose of limiting complexity, we only addressed the feedback element by introducing it with an anthropomorphic virtual agent (human-like aspect), because research shows that virtual agents (VA’s) can influence behavioural change [17], or even induce emotions on humans both through the use of feedback provided and their facial expressions, which can interpreted in the same way as of humans’ [2]. By pairing the VA with the gamification concept, we wanted to 1) create a VA that is likely to be well-received by children (appearance and behaviour), and 2) have the immediate feedback that games have, so we can give children an assessment of their actions in real-time, as opposed to waiting for feedback from someone (traditional teaching), and with this give students more chances to succeed [32, 43]. Our final system consisted on a virtual environment, where children formed words that corresponded to a given image. In order to measure the impact that the VA had on engagement, the system was developed in two versions: one version of the system was limited to provide a simple feedback environment, where the VA provided feedback, by responding with simple phrases (i.e. “correct” or “incorrect”); for the second version, the VA had a more complex approach where it tried to encourage children to complete the word – a motivational feedback - even when they weren’t succeeding. Lastly we conducted a field study with two groups of children, where one group tested the version with the simple feedback, and the other group tested the ‘motivational’ version of the system. We used a quantitative approach to analyze the collected data that measured the engagement, based on the number of tasks (words) completed and time spent with system. The results of the evaluation showed that the use of motivational feedback may carry a positive effect on engaging children.

Do children copy an expert or a majority? Examining selective learning in instrumental and normative contexts

This study examined whether instrumental and normative learning contexts differentially influence 4- to 7-year-old children’s social learning strategies; specifically, their dispositions to copy an expert versus a majority consensus. Experiment 1 (N = 44) established that children copied a relatively competent “expert” individual over an incompetent individual in both kinds of learning context. In experiment 2 (N = 80) we then tested whether children would copy a competent individual versus a majority, in each of the two different learning contexts. Results showed that individual children differed in strategy, preferring with significant consistency across two different test trials to copy either the competent individual or the majority. This study is the first to show that children prefer to copy more competent individuals when shown competing methods of achieving an instrumental goal (Experiment 1) and provides new evidence that children, at least in our “individualist” culture, may consistently express either a competency or majority bias in learning both instrumental and normative information (Experiment 2). This effect was similar in the instrumental and normative learning contexts we applied.

A virtual agent to sustaining children's engagement in language learning

In this thesis we aimed to explore the potential of gamification - defined as “the use of game elements in non-game contexts” [30] - in increasing children's (aged 5 to 6) engagement with the task. This is mainly due to the fact that our world is living a technological era, and videogames are an example of this engagement by being able to maintain children’s (and adults) engagement for hours straight. For the purpose of limiting complexity, we only addressed the feedback element by introducing it with an anthropomorphic virtual agent (human-like aspect), because research shows that virtual agents (VA’s) can influence behavioural change [17], or even induce emotions on humans both through the use of feedback provided and their facial expressions, which can interpreted in the same way as of humans’ [2]. By pairing the VA with the gamification concept, we wanted to 1) create a VA that is likely to be well-received by children (appearance and behaviour), and 2) have the immediate feedback that games have, so we can give children an assessment of their actions in real-time, as opposed to waiting for feedback from someone (traditional teaching), and with this give students more chances to succeed [32, 43]. Our final system consisted on a virtual environment, where children formed words that corresponded to a given image. In order to measure the impact that the VA had on engagement, the system was developed in two versions: one version of the system was limited to provide a simple feedback environment, where the VA provided feedback, by responding with simple phrases (i.e. “correct” or “incorrect”); for the second version, the VA had a more complex approach where it tried to encourage children to complete the word – a motivational feedback - even when they weren’t succeeding. Lastly we conducted a field study with two groups of children, where one group tested the version with the simple feedback, and the other group tested the ‘motivational’ version of the system. We used a quantitative approach to analyze the collected data that measured the engagement, based on the number of tasks (words) completed and time spent with system. The results of the evaluation showed that the use of motivational feedback may carry a positive effect on engaging children.

The Butterfly Effect on Peace Education

The objective of this paper on peace education is to generate a reflection, through the metaphor of the butterfly effect, on the importance of educating for peace during the change process of human beings and society.  It proposes education for peace as a human right, an experience and learning process that is put into practice by human beings.  It aims at changing attitudes and actions to create harmonious relationships based on the respect and recognition of human rights, and the freedom and dignity of every person.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.

Evaluation Metrics and Transfer Learning for Sales Prediction

Sales prediction plays a huge role in modern business strategies. One of it's many use cases revolves around estimating the effects of promotions. While promotions generally have a positive effect on sales of the promoted product, they can also have a negative effect on those of other products. This phenomenon is calles sales cannibalisation. Sales cannibalisation can pose a big problem to sales forcasting algorithms. A lot of times, these algorithms focus on sales over time of a single product in a single store (a couple). This research focusses on using knowledge of a product across multiple different stores. To achieve this, we applied transfer learning on a neural model developed by Kantar Consulting to demo an approach to estimating the effect of cannibalisation. Our results show a performance increase of between 10 and 14 percent. This is a very good and desired result, and Kantar will use the approach when integrating this test method into their actual systems.