How Learning Styles, Chemistry Attitudes and Experiences, Confidence, and Demographics Correlate with Academic Success in First and Second Year Chemistry Courses

At Brock University, the Faculty of Mathematics and Science currently has one of the highest percentages of students on academic probation, with many students reporting the most difficulty with Introductory Chemistry in first year and Organic Chemistry in second year. To identify strategies to improve students' performance and reduce the number of students on academic probation, a multi-year research project was undertaken involving several chemistry courses. Students were asked to complete three questionnaires, and provide consent to obtain their final Chemistry grade from the Registrar's Office. Research began at the end of the 2007-08 academic year with CHEM IPOO, and in the 2008-09 academic year, students in the larger CHEM IF92 Introductory Chemistry course were invited to participate in this research near the beginning of the academic year. Students who went on to take second year Organic and Analytical Chemistry were asked to complete these questionnaires in each second year course. The three questionnaires included the Kolb Learning Styles Inventory (Kolb, 1984) modified to include specific reference to Chemistry in each question, Dalgety, ColI, and Jones' (2002) Chemistry Attitudes and Experiences Questionnaire (CAEQ), and lastly, a demographic survey. Correlations were found between learning style and academic success; concrete learners were not as successful as abstract learners. Differences were noted between females and males with respect to learning styles, academic success, and confidence. Several differences were also noted between those who are the First in the Family to attend university and those who are not First in the Family to attend university.

Learning and Experiential Outcomes of Face-to-Face Versus Online Communications Courses

Higher education is rapidly trending toward the implementation of online (OL) courses and a blended facilitation style that incorporates both OL and face-to-face (FTF) classes. Though previous studies have explored the benefits and pitfalls of OL and blended learning formats from institutional, teacher, and student perspectives, scant research has examined learning outcomes for OL and FTF courses sharing identical content. This study used an explanatory mixed methods design—including pre- and post-test assessments, a questionnaire, and interviews—to explore similarities and differences in participant and teacher perceptions and outcomes (gain scores and final grades) of OL versus traditional FTF Communications courses, and to examine effects of students’ age and gender on learning preference and performance. Data collection occurred over a 4-month period and involved 183 student and 2 professor participants. The study used an SPSS program for data analysis and created a Microsoft Excel document to record themes derived from the questionnaire and interviews. Quantitative findings suggest there are no significant differences in gain scores, final grades, or other learning outcomes when comparing OL and FTF versions of identical Communications courses; however, qualitative findings indicate differences between facilitation styles based on student and professor perception. The study sheds light on student and faculty perceptions of facilitation styles and suggests areas for potential improvements in FTF- and OL-facilitated courses. The study ultimately recommends that students and faculty should have options when it comes to preferred delivery of course material.

The Personal tutorial guidance sessions in the framework of the teaching-learning field of the Bologna process: University of Minho (first cycle) courses students’ perceptions

The Bologna Process defends the adoption of a higher education in teaching-learning methodologies that – in contraposition to the previous model based on the transmission of knowledge, which for being essentially theoretical, gives the student a passive role in the knowledge construction process – allows a (pro) active, autonomous and practical learning, where the student acquires and develops his competences. The personal tutorial guidance sessions are included in the teaching contact hours. This abstract presents a study about the University of Minho (first cycle) Courses Students’ perceptions of the personal tutorial guidance sessions’ relevance in the scope of the learning-teaching process, so as to confirm if the implementation/implantation of the commonly called tutorial (type) education, as an approach to an active, autonomous and practical learning, is sensed by the learners themselves

Adaption of Courses and Learning Environment to the User Context in dotLRN

The explosive growth of Internet during the last years has been reflected in the ever-increasing amount of the diversity and heterogeneity of user preferences, types and features of devices and access networks. Usually the heterogeneity in the context of the users which request Web contents is not taken into account by the servers that deliver them implying that these contents will not always suit their needs. In the particular case of e-learning platforms this issue is especially critical due to the fact that it puts at stake the knowledge acquired by their users. In the following paper we present a system that aims to provide the dotLRN e-learning platform with the capability to adapt to its users context. By integrating dotLRN with a multi-agent hypermedia system, online courses being undertaken by students as well as their learning environment are adapted in real time

A teaching/learning support tool for introductory programming courses

In This work we present a Web-based tool developed with the aim of reinforcing teaching and learning of introductory programming courses. This tool provides support for teaching and learning. From the teacher's perspective the system introduces important gains with respect to the classical teaching methodology. It reinforces lecture and laboratory sessions, makes it possible to give personalized attention to the student, assesses the degree of participation of the students and most importantly, performs a continuous assessment of the student's progress. From the student's perspective it provides a learning framework, consisting in a help environment and a correction environment, which facilitates their personal work. With this tool students are more motivated to do programming

A video-based biometric authentication for e-learning web applications

In the last years there was an exponential growth in the offering of Web-enabled distance courses and in the number of enrolments in corporate and higher education using this modality. However, the lack of efficient mechanisms that assures user authentication in this sort of environment, in the system login as well as throughout his session, has been pointed out as a serious deficiency. Some studies have been led about possible biometric applications for web authentication. However, password based authentication still prevails. With the popularization of biometric enabled devices and resultant fall of prices for the collection of biometric traits, biometrics is reconsidered as a secure remote authentication form for web applications. In this work, the face recognition accuracy, captured on-line by a webcam in Internet environment, is investigated, simulating the natural interaction of a person in the context of a distance course environment. Partial results show that this technique can be successfully applied to confirm the presence of users throughout the course attendance in an educational distance course. An efficient client/server architecture is also proposed. © 2009 Springer Berlin Heidelberg.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Autenticação biométrica de usuários em sistemas de E-learning baseada em reconhecimento de faces a partir de vídeo

Pós-graduação em Ciência da Computação - IBILCE