Since the introduction of mobile devices, alternative language learning methods have been developed and have evolved alongside traditional language classroom education. This has presented academics with the unique opportunity to study new methods of second language acquisition compared to more traditional face-to-face language instruction. The growing technology possibilities have contributed to what is called Mobile Assisted Language Learning (MALL), where learners can easily study a new language with the use of a personal electronic device such as a laptop or phone. Studies about such learning tools, for example Rosetta Stone, Duolingo or Babbel have been carried out for several years to for example test their effectiveness. Among research studies, one well-known study by Vesselinov and Grego (2012) looked at the effectiveness of Duolingo, evaluating the statement Duolingo makes that 34 hours of studying with the tool is equivalent to a semester in a university classroom (Vesselinov & Grego, 2012). Even though various studies about the well-known language learning application (app) Duolingo have been conducted, gaps in research are still present.The current study aims to find out if studying German for one semester (14 weeks) with Duolingo is equivalent to one semester in a beginner face-to-face class, German 101, at a university level. At the end of the semester, both groups took the German 101 final exam and the Duolingo placement test to measure German learning. After analyzing results of the German 101 exam, results showed that learners from the face-to-face class achieved higher language knowledge in the tested skills writing and reading than the participants who studied with Duolingo for the same period of time. However, no substantial differences were found between groups for the vocabulary and grammar section of the final exam and the Duolingo test. The survey reported that participants who studied with Duolingo all agreed that they liked the experience, especially studying at their own pace. Concerns were also mentioned, however, for example that Duolingo is only good for beginner learners, it doesn’t provide interactions with others, and there is no teacher to ask for explicit feedback.

This thesis aims to contribute to the field of interaction design by exploring the use of nonverbal cues in FTF communication with the aim of generating knowledge that can be used as an alternative approach for assessing and designing text-based CMC media. To achieve this goal, movement in is analysed in the nonverbal and collaborative dimensions of FTF communication. This presents the possibility to assess text-based CMC media based on a better understanding of the use of nonverbal cues and FTF communication as a standard. The assessment and design based on this concept is tested in the design phase. This process provides a platform for discussion and evaluation of an alternative approach for designing text-based CMC media with a focus on interaction between communicators.

The purpose of this quantitative study was to determine if there were significant differences in student success in face-to-face and online biology courses as categorized by gender, major, and age; and as measured by lecture grades, lab grades, and final course grades. The data used for analyses included data from 170 face-to-face sections and 127 online sections from a biology course during the fall and spring semesters beginning fall 2008 through spring 2011. Researchers have reported mixed findings in previous studies juxtaposing online and face-to-face course delivery formats, from no significant differences to differences in grades, learning styles, and satisfaction levels. Four research questions guided this study with data analysis involving t-tests for independent groups and chi-square tests. This researcher noted significant differences in the results of this study: grades, success rates by gender, success rates by health and nonhealth majors, and nontraditional age (≥25) success rate were higher for students in the face-to-face courses; and the attrition rate was higher for students in the online course sections. There was no significant difference found in the success rate for traditional age (<25) students in the face-to-face sections compared to those in the online sections.

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Faces are special objects of interest. Developing automated systems for detecting and recognizing faces is useful in a variety of application domains including providing aid to visually-impaired people and managing large-scale collections of images. Humans have a remarkable ability to detect and identify faces in an image, but related automated systems perform poorly in real-world scenarios, particularly on faces that are difficult to detect and recognize. Why are humans so good? There is general agreement in the cognitive science community that the human brain uses the context of the scene shown in an image to solve the difficult cases of detection and recognition. This dissertation focuses on emulating this approach by using different kinds of contextual information for improving the performance of various approaches for face detection and face recognition. For the face detection problem, we describe an algorithm that employs the easyto- detect faces in an image to find the difficult-to-detect faces in the same image. For the face recognition problem, we present a joint probabilistic model for image-caption pairs. This model solves the difficult cases of face recognition in an image by using the context generated from the caption associated with the same image. Finally, we present an effective solution for classifying the scene shown in an image, which provides useful context for both of the face detection and recognition problems.

Machine face recognition has traditionally been studied under the assumption of a carefully controlled image acquisition process. By controlling image acquisition, variation due to factors such as pose, lighting, and background can be either largely eliminated or specifically limited to a study over a discrete number of possibilities. Applications of face recognition have had mixed success when deployed in conditions where the assumption of controlled image acquisition no longer holds. This dissertation focuses on this unconstrained face recognition problem, where face images exhibit the same amount of variability that one would encounter in everyday life. We formalize unconstrained face recognition as a binary pair matching problem (verification), and present a data set for benchmarking performance on the unconstrained face verification task. We observe that it is comparatively much easier to obtain many examples of unlabeled face images than face images that have been labeled with identity or other higher level information, such as the position of the eyes and other facial features. We thus focus on improving unconstrained face verification by leveraging the information present in this source of weakly supervised data. We first show how unlabeled face images can be used to perform unsupervised face alignment, thereby reducing variability in pose and improving verification accuracy. Next, we demonstrate how deep learning can be used to perform unsupervised feature discovery, providing additional image representations that can be combined with representations from standard hand-crafted image descriptors, to further improve recognition performance. Finally, we combine unsupervised feature learning with joint face alignment, leading to an unsupervised alignment system that achieves gains in recognition performance matching that achieved by supervised alignment.

Face recognition research using automatic or semi-automatic techniques has emerged over the last two decades. One reason for growing interest in this topic is the wide range of possible applications for face recognition systems. Another reason is the emergence of affordable hardware, supporting digital photography and video, which have made the acquisition of high-quality and high resolution 2D images much more ubiquitous. However, 2D recognition systems are sensitive to subject pose and illumination variations and 3D face recognition which is not directly affected by such environmental changes, could be used alone, or in combination with 2D recognition. Recently with the development of more affordable 3D acquisition systems and the availability of 3D face databases, 3D face recognition has been attracting interest to tackle the limitations in performance of most existing 2D systems. In this research, we introduce a robust automated 3D Face recognition system that implements 3D data of faces with different facial expressions, hair, shoulders, clothing, etc., extracts features for discrimination and uses machine learning techniques to make the final decision. A novel system for automatic processing for 3D facial data has been implemented using multi stage architecture; in a pre-processing and registration stage the data was standardized, spikes were removed, holes were filled and the face area was extracted. Then the nose region, which is relatively more rigid than other facial regions in an anatomical sense, was automatically located and analysed by computing the precise location of the symmetry plane. Then useful facial features and a set of effective 3D curves were extracted. Finally, the recognition and matching stage was implemented by using cascade correlation neural networks and support vector machine for classification, and the nearest neighbour algorithms for matching. It is worth noting that the FRGC data set is the most challenging data set available supporting research on 3D face recognition and machine learning techniques are widely recognised as appropriate and efficient classification methods.