Learning analytics focuses on using existing accumulated learning data through analysis related techniques to provide appropriate information to learners and facilitating learners to adjust their learning strategies (personalization and adaptation) in improving learning effectiveness. Through learning analytics, activities of teaching, learning, and management processes will be significantly changed. Although learning analytics has been considered one of the six critical trends (ebook, mobile learning, augmented reality, game-based learning, natural user interface, and learning analytics) of high education in the near future, there are only few studies focusing on exploring learning analytics related issues. To address this void, this thesis aims for analyzing and designing a personalized mobile learning analytics system that is a mobile application prototyping system developed by incorporating concepts of learning analytics and mobile learning. User requirements of the prototyping system are collected by database analysis (LMS platform), focus groups (users of mobile learning), and expert interviews (experts and practitioners in e-learning domain). Those collected requirements have been translated into system functionalities and then they have been appropriately implemented through adequate system development tools. Finally, the implemented prototyping system has been tested and validated by experts and practitioners in e-learning domain. Therefore, this study has significant contributions on conducting an in-depth system analysis and design relating to mobile learning with learning analytics and validating the feasibility of learning analytics by the prototyping approach. We suggest that academics and practitioners can conduct more in-depth research on investigating learning analytics related issues based on the findings of this study.

The number of American students with insufficient post-secondary mathematical abilities is increasing and the related rate of student attrition increases alongside the upsurge in college developmental programs. As a consequence, the demand for quality remedial mathematics classes is also growing. Institutions that place learners into remedial classes must also fund these same programs and are increasingly faced with disgruntled students, the appearance of having lower standards, and a demoralized faculty. The legal implications concerning placement and access have gone as far as litigation over student rights. The threat of performance based funding means that educational institutions are in need of demonstrably effective mathematical remediation techniques. This study examines the effect of pedagogical style for college-level remedial mathematics students and the effect of the chosen assessment method in determining student success. Specifically, this study explains student achievement for college students exposed to a pedagogical style from either the constructivist or behaviorist foundation as measured with short-answer, rote-knowledge questions and with long-answer, deductive-reasoning questions. Furthermore, consideration of student self-efficacy is investigated in order to account for any variation in instructional method. Ultimately, this study describes the effects of both instruction type and assessment method on the success of college-level remedial mathematics students. The findings in this study reveal quality teaching is of paramount importance in educating the remedial college student. Students from both methods, with instruction being performed with high fidelity, demonstrated statistically significant improvement over the semester. Moreover, the findings in this study further reveal that remedial students with strong reasons to succeed (combined with the quality teaching method) find success in the developmental mathematics classroom regardless of assessment method. In fact, though students tend to score higher on short-answer questions than extended-answer questions, the amount of improvement after a semester of quality teaching is nearly equal in question types under both instructional methods.

With the proliferation of electronic commerce and knowledge economy environments, both persons and organizations increasingly have generated and consumed large amounts of online information, typically available as textual documents. To manage this rapid growth of the number of textual documents, people often use categories or folders to organize their documents. These document grouping behaviors are intentional acts that reflect the persons¡¦ (or organizations¡¦) preferences with regard to semantic coherency, or relevant groupings between subjects. For this thesis, we design and implement an ontology-based personalized document clustering (OnPEC) technique by incorporating both an individual user¡¦s partial clustering and an ontology into the document clustering process. Our use of a target user¡¦s partial clustering supports the personalization of document categorization, whereas our use of the ontology turns document clustering from a feature-based to a concept-based approach. In addition, we combine two hierarchical agglomerative clustering (HAC) approaches (i.e., pre-cluster-based and atomic-based) in our proposed OnPEC technique. Using the clustering effectiveness achieved by a traditional content-based document clustering technique and previously proposed feature-based document clustering (PEC) techniques as performance benchmarks, we find that use of partial clusters improves document clustering effectiveness, as measured by cluster precision and cluster recall. Moreover, for both OnPEC and PEC techniques, the clustering effectiveness of pre-cluster-based HAC methods greatly outperforms that of atomic-based HAC methods.

With the advances in information and networking technologies and the proliferation of e-commerce and knowledge management applications, individuals and organizations generate and acquire tremendous amount of online information that is typically available as textual documents. To manage the ever-increasing volume of documents, an individual or organization frequently organizes his/her documents into a set or hierarchy of categories in order to facilitate document management and subsequent information access and browsing. Furthermore, document clustering is an intentional act that reflects individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document-clustering must consider individual preferences for supporting personalization in document categorization and should be capable of organizing documents into a category hierarchy. However, document-clustering research traditionally has been anchored in analyses of document content. As a consequence, most of existing document-clustering techniques are not tailored to individuals¡¦ preferences and therefore are unable to facilitate personalization. On the other hand, existing document-clustering techniques generally are designed to generate from a document collection a set of document clusters rather than a hierarchy of document clusters. In response, we develop in this study a hierarchical personalized document-clustering (HPEC) technique that takes into account an individual¡¦s folder hierarchy representing the individual¡¦s categorization preferences and produces document-clusters in a hierarchical structure for the target individual. Our empirical evaluation results suggest that the proposed HPEC technique outperformed its benchmark technique (i.e., HAC+P) in cluster recall while maintaining the same level of cluster precision and location discrepancy as its benchmark technique did.

According to the context theory of classification, the document-clustering behaviors of individuals not only involve the attributes (including contents) of documents but also depend on who is doing the task and in what context. Thus, effective document-clustering techniques need to be able to take into account users¡¦ categorization preferences and thus can generate document clusters from different preferential perspectives. The Preference-Anchored Document Clustering (PAC) technique was proposed for supporting preference-based document-clustering. Specifically, PAC takes a user¡¦s categorization preference into consideration and subsequently generates a set of document clusters from this specific preferential perspective. In this study, we attempt to investigate two research questions concerning the PAC technique. The first research question investigates ¡§whether the incorporation of the broader-term expansion (i.e., the proposed PAC2 technique in this study) will improve the effectiveness of preference-based document-clustering, whereas the second research question is ¡§whether the use of a statistical-based thesaurus constructed from a larger document corpus will improve the effectiveness of preference-based document-clustering.¡¨ Compared with the effectiveness achieved by PAC, our empirical results show that the proposed PAC2 technique neither improves nor deteriorates the effectiveness of preference-based document-clustering when the complete set of anchoring terms is used. However, when only a partial set of anchoring terms is provided, PAC2 cannot improve and even deteriorate the effectiveness of preference-based document-clustering. As to the second research question, our empirical results suggest the use of a statistical-based thesaurus constructed from a larger document corpus (i.e., the ACM corpus consisting of 14,729 documents) does not improve the effectiveness of PAC and PAC2 for preference-based document-clustering.

To manage the ever-increasing volume of documents, organizations and individuals typically organize documents into categories (or category hierarchies) to facilitate their document management and support subsequent document retrieval and access. Document clustering is an intentional act that should reflect individuals¡¦ preferences with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective document clustering techniques need to take into account a user¡¦s categorization context defined by or relevant to the target task under consideration. However, existing document clustering techniques generally anchor in pure content-based analysis and therefore are not able to facilitate personalized or context-aware document clustering. In response, we design, implement and empirically evaluate three document clustering techniques capable of facilitating personalized or contextual document clustering. First, we extend an existing document clustering technique (specifically, the partial-clustering-based personalized document-clustering (PEC) approach) and propose the Collaborative Filtering¡Vbased personalized document-Clustering (CFC) technique to overcome the problem of small-sized partial clustering encountered by the PEC technique. Particularly, the CFC technique expands the size of a user¡¦s partial clustering based on the partial clusterings of other users with similar categorization preferences. Second, to support contextual document clustering, we design and implement a Context-Aware document-Clustering (CAC) technique by taking into consideration a user¡¦s categorization preference (i.e., a set of anchoring terms) relevant to the context of a target task and a statistical-based thesaurus constructed from the World Wide Web (WWW) via a search engine. Third, in response to the problem of small-sized set of anchoring terms which can greatly degrade the effectiveness of the CAC technique, we extend CAC and propose a Collaborative Filtering-based Context-Aware document Clustering (CF-CAC) technique. Our empirical evaluation results suggest that our proposed CFC, CAC, and CF-CAC techniques better support the need of personalized and contextual document clustering than do their benchmark techniques.

Abstract By taking the advantages of the Internet technology, the online bookstores developed personalized services, such as providing information for making transaction decision which lowers the consuming cost, fastens the customer loyalty, and creates competitiveness. The purpose of setting up the service mechanism is to win back the consumer. Therefore, understood the customer¡¦s usage intention on the personalized services would benefit the online bookstores to build up their personalized service mechanism and to improve the practical values of the personalized services. ¡@¡@Utilizing Transaction Cost Theory as the extended viewpoint of Technology Acceptance Model, this thesis not only analyzed if the online bookstore¡¦s personalized services impact the consumer¡¦s transactional cost, but also investigated if the benefits arisen from the personalized services vary by other factors. By means of the analysis of the transaction cost and the investigation of the consumer¡¦s useful recognition, the usage intention of the online bookstore¡¦s personalized services can be reasoned out. In this thesis, the acceptability of the online bookstore¡¦s personalized services is realized, the possible factors that could affect the consumer¡¦s usage intention are discussed. The major findings are: the benefits of transaction cost arisen from personalized services were affected by the transactional frequency. More transactional activities, more benefits arisen from the personalized services were observed. Consumers believe that the personalized services provided by online bookstores can lower their transactional cost while their subjectiveness and need are being cared. In addition, different levels of consumer¡¦s useful recognition were obtained while the personal feelings or realizations vary with the contents of the personalized services. According to the findings, the online bookstore¡¦s personalized services are highly recognizable by consumers, especially at the fields of ¡§Purchase & Process Feedback Service¡¨ (PPFS) and ¡§One-by-one Customer Service¡¨ (OCS). The useful recognition for ¡§E-papers Service¡¨ is also sound but relatively lower than that of the others. Referring to the Technology Acceptance Model, the findings show that consumers are highly intentioned to use the online bookstore¡¦s personalized services.

With the proliferation of an electronic commerce and knowledge economy environment, both organizations and individuals generate and consume a large amount of online information, typically available as textual documents. To manage the ever-increasing volume of documents, organizations and individuals typically organize their documents into categories to facilitate document management and subsequent information access and browsing. However, document grouping behaviors are intentional acts, reflecting individuals¡¦ (or organizations¡¦) preferential perspective on semantic coherency or relevant groupings between subjects. Thus, an effective document clustering needs to address the described preferential perspective on document grouping and support personalized document clustering. In this thesis, we designed and implemented a personalized document clustering approach by incorporating individual¡¦s partial clustering into the document clustering process. Combining two document representation methods (i.e., feature refinement and feature weighting) with two clustering processes (i.e., pre-cluster-based and atomic-based), four personalized document clustering techniques are proposed. Using the clustering effectiveness achieved by a traditional content-based document clustering technique as performance benchmarks, our evaluation results suggest that use of partial clusters would improve the document clustering effectiveness. Moreover, the pre-cluster-based technique outperforms the atomic-based one, and the feature weighting method for document representation achieves a higher clustering effectiveness than the feature refinement method does.

Because of the situation around the world with war and poorness many people need to leave their homeland and find safeness in other countries. A big amount of this group is children without company of an adult, so called unaccompanied children. The numbers of unaccompanied children who seek safeness in Sweden grow every year. The aim of this study was to investigate how social workers in four municipalities in south of Sweden experience their work with investigating the needs of unaccompanied children. The aim was also to investigate how the social workers experience their opportunities to meet the children’s needs with personalized interventions. The study is built on semi-structured interviews with six social workers who all have experience or are now working with investigating unaccompanied children. The results of the interviews were analyzed using a system theory perspective and Andrew Abbotts profession theory. We also used the terms grassroots bureaucrats and discretion. The results of our study has shown us that the social workers often feel a certain restriction when it comes to their ability to meet the children’s needs because of the lack of available accommodations. The study also shown that the social workers experience a limitation when it comes to the authority they have as their decisions must be approved by others and not based solely on the individual workers assessment.   The social workers in our study also experience that their work is not taken as seriously as others investigation of children from Swedish families which influence the status and legitimacy that they describe that their profession should have.

Despite considerable effort considerable cost in both time and money, as many as two out of three persons with type 1 diabetes are not in control of their disease. As a result, 40% of these individuals will go on to develop at least one serious complication including retinopathy, nephropathy, neuropathy and cardiomyopathy. It is further estimated that as much as $4 billion could be saved annually if all persons with type 1 diabetes in the US were properly controlled. Adequate treatment of type 1 diabetes is predicated on the estimation of three clinical insulin therapy parameters: the basal dose, the insulin sensitivity factor and the insulin-to-carbohydrate ratio. Currently, these therapy parameters are determined by iterative titration procedures based on expert opinion. Unfortunately, there is evidence suggesting that for the majority of individuals, these titration protocols do not provide good results. In this work we develop an alternative to traditional insulin titration protocols that allows clinical insulin therapy parameters to be estimated directly from a set of easily acquired measurements. First, a simple model of type 1 diabetes is used to derive a series of equations connecting the model's parameters to the clinically important insulin therapy parameters of insulin sensitivity factor, insulin-to-carbohydrate ratio and basal insulin dose. The simplifying assumptions used to derive these equations are tested and shown to be valid and the Fisher Information Matrix is used to demonstrate parameter identifiability. Parameter estimation is then performed on two cohorts of virtual subjects, as well as two segments of real continuous glucose monitoring data from a person with type 1 diabetes. Identification of the true insulin therapy parameters is successful under most conditions for both cohorts of virtual subjects. Parameter estimation for one of the two segments of real continuous glucose monitoring data is also successful. Finally, because continuous glucose monitors are instrumental to successful implementation of our insulin therapy framework, the physiological environment in which continuous glucose monitoring takes place is modeled and a fundamental limitation on measurement precision is shown to exist. An examination of physiological variability in the parameters indicates that many of the challenges observed in real world continuous glucose monitoring may have a relationship to changes in capillary bed perfusion. A rationale for anecdotally reported sensor faults is also proposed based on the physical mechanisms explored. / text