Biochemistry 2015 APR Self-Study & Documents

UNM Biochemistry APR self-study report, review team report, response to review report, and initial action plan for Fall 2015, fulfilling requirements of the Higher Learning Commission

Learning Microscopic Pathology: Scaffolding the Early Development of Expertise in Medical Image Interpretation

Siirretty Doriast

An analysis of students’ behaviour in a Learning Management System through Process Mining

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe exponential growth and transformation of the Internet and information technology in recent years led to the development of several analytical tools. As is the case with process mining, it emerged to fulfill the need to extract and analyze information from event logs by representing it in the form of process models. Process mining is an acclaimed tool and proved crucial in several areas, from healthcare to manufacturing and finance. Nevertheless, and despite the crucial role of digital systems in supporting learning activities and generating large amounts of data about learning processes, limited research focused on process mining applied to the educational context. Therefore, the aim of this dissertation is to apply a process-oriented approach and demonstrate the applicability of process mining techniques to explore and analyze students’ behavior and interaction patterns, based on data collected from Moodle, the widely used Learning Management System. We cover definitions of process mining, education, and a detailed search of the existing literature on educational process mining during this work. Furthermore, the paper analyzes and discusses the findings of the study that combines process mining techniques, specifically process discovery implanted in the Disco tool, with cluster analysis. Through the application of these two techniques, it was possible to recognize the relationship between the students’ behavior registered in the process models and the success of the students in the course, along with the general and specific information about the students’ learning paths. Besides, we obtained findings that allow us to predict the group of students at risk of failing. Finally, with the analysis of these results, we were able to provide improvement proposals and recommendations to enhance the learning experience

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Public Health Instructors\u27 Attitudes Regarding Online Instructional Course Design: A Collective Case Study

The purpose of this collective case study was to describe instructors’ attitudes regarding Keller’s personalized system of instruction (PSI) for a sample of online master’s-level public health instructors at an institution of higher education. The central research question was how do public health instructors describe their attitudes regarding personalized system of instruction and online graduate education? The institution selected for this investigation was “University A.” The theory guiding this study was Keller’s personalized system of instruction (PSI), as it features five elements for student-centered course design in higher education learning. The elements include: (a) self-pacing, (b) unit mastery, (c) lectures and demonstrations to motivate students, (d) learning from assigned textbooks and written materials, and (e) use of proctors to assist with testing, grading, tutoring students and answering student questions. This study recruited 13 instructors for virtual, semi-structured interviews, in which participants shared their attitudes regarding the five elements of PSI. This study recruited a virtual focus group consisting of five instructors to explore attitudes further. This study featured document analysis of online master’s-level public health syllabi. Manual coding was used to develop themes from transcripts and documents. Six themes emerged from the triangulation of data: (a) time, (b) assessment considerations, (c) multiple sources of instruction, (d) instructor roles, (e) strategies to motivate students, and (f) personalized online education. The results may assist with the future design and development of flexible, personalized online master’s-level public health coursework

Utilizing educational technology in computer science and programming courses : theory and practice

There is one thing the Computer Science Education researchers seem to agree: programming is a difficult skill to learn. Educational technology can potentially solve a number of difficulties associated with programming and computer science education by automating assessment, providing immediate feedback and by gamifying the learning process. Still, there are two very important issues to solve regarding the use of technology: what tools to use, and how to apply them? In this thesis, I present a model for successfully adapting educational technology to computer science and programming courses. The model is based on several years of studies conducted while developing and utilizing an exercise-based educational tool in various courses. The focus of the model is in improving student performance, measured by two easily quantifiable factors: the pass rate of the course and the average grade obtained from the course. The final model consists of five features that need to be considered in order to adapt technology effectively into a computer science course: active learning and continuous assessment, heterogeneous exercise types, electronic examination, tutorial-based learning, and continuous feedback cycle. Additionally, I recommend that student mentoring is provided and cognitive load of adapting the tools considered when applying the model. The features are classified as core components, supportive components or evaluation components based on their role in the complete model. Based on the results, it seems that adapting the complete model can increase the pass rate statistically significantly and provide higher grades when compared with a “traditional” programming course. The results also indicate that although adapting the model partially can create some improvements to the performance, all features are required for the full effect to take place. Naturally, there are some limits in the model. First, I do not consider it as the only possible model for adapting educational technology into programming or computer science courses. Second, there are various other factors in addition to students’ performance for creating a satisfying learning experience that need to be considered when refactoring courses. Still, the model presented can provide significantly better results, and as such, it works as a base for future improvements in computer science education.Ohjelmoinnin oppimisen vaikeus on yksi harvoja asioita, joista lähes kaikki tietojenkäsittelyn opetuksen tutkijat ovat jokseenkin yksimielisiä. Opetusteknologian avulla on mahdollista ratkaista useita ohjelmoinnin oppimiseen liittyviä ongelmia esimerkiksi hyödyntämällä automaattista arviointia, välitöntä palautetta ja pelillisyyttä. Teknologiaan liittyy kuitenkin kaksi olennaista kysymystä: mitä työkaluja käyttää ja miten ottaa ne kursseilla tehokkaasti käyttöön? Tässä väitöskirjassa esitellään malli opetusteknologian tehokkaaseen hyödyntämiseen tietojenkäsittelyn ja ohjelmoinnin kursseilla. Malli perustuu tehtäväpohjaisen oppimisjärjestelmän runsaan vuosikymmenen pituiseen kehitys- ja tutkimusprosessiin. Mallin painopiste on opiskelijoiden suoriutumisen parantamisessa. Tätä arvioidaan kahdella kvantitatiivisella mittarilla: kurssin läpäisyprosentilla ja arvosanojen keskiarvolla. Malli koostuu viidestä tekijästä, jotka on otettava huomioon tuotaessa opetusteknologiaa ohjelmoinnin kursseille. Näitä ovat aktiivinen oppiminen ja jatkuva arviointi, heterogeeniset tehtävätyypit, sähköinen tentti, tutoriaalipohjainen oppiminen sekä jatkuva palautesykli. Lisäksi opiskelijamentoroinnin järjestäminen kursseilla ja järjestelmän käyttöönottoon liittyvän kognitiivisen kuorman arviointi tukevat mallin käyttöä. Malliin liittyvät tekijät on tässä työssä lajiteltu kolmeen kategoriaan: ydinkomponentteihin, tukikomponentteihin ja arviontiin liittyviin komponentteihin. Tulosten perusteella vaikuttaa siltä, että mallin käyttöönotto parantaa kurssien läpäisyprosenttia tilastollisesti merkittävästi ja nostaa arvosanojen keskiarvoa ”perinteiseen” kurssimalliin verrattuna. Vaikka mallin yksittäistenkin ominaisuuksien käyttöönotto voi sinällään parantaa kurssin tuloksia, väitöskirjaan kuuluvien tutkimusten perusteella näyttää siltä, että parhaat tulokset saavutetaan ottamalla malli käyttöön kokonaisuudessaan. On selvää, että malli ei ratkaise kaikkia opetusteknologian käyttöönottoon liittyviä kysymyksiä. Ensinnäkään esitetyn mallin ei ole tarkoituskaan olla ainoa mahdollinen tapa hyödyntää opetusteknologiaa ohjelmoinnin ja tietojenkäsittelyn kursseilla. Toiseksi tyydyttävään oppimiskokemukseen liittyy opiskelijoiden suoriutumisen lisäksi paljon muitakin tekijöitä, jotka tulee huomioida kurssien uudelleensuunnittelussa. Esitetty malli mahdollistaa kuitenkin merkittävästi parempien tulosten saavuttamisen kursseilla ja tarjoaa sellaisena perustan entistä parempaan opetukseen

Predicting Academic Performance of Students from VLE Big Data using Deep Learning Models

The abundance of accessible educational data, supported by the technology-enhanced learning platforms, provides opportunities to mine learning behavior of students, addressing their issues, optimizing the educational environment, and enabling data-driven decision making. Virtual learning environments complement the learning analytics paradigm by effectively providing datasets for analysing and reporting the learning process of students and its reflection and contribution in their respective performances. This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures for early intervention of such cases. The results show the proposed model to achieve a classification accuracy of 84%-93%. We show that a deep artificial neural network outperforms the baseline logistic regression and support vector machine models. While logistic regression achieves an accuracy of 79.82% - 85.60%, the support vector machine achieves 79.95% - 89.14%. Aligned with the existing studies - our findings demonstrate the inclusion of legacy data and assessment-related data to impact the model significantly. Students interested in accessing the content of the previous lectures are observed to demonstrate better performance. The study intends to assist institutes in formulating a necessary framework for pedagogical support, facilitating higher education decision-making process towards sustainable education