The study was carried out as part of the research project &#34;SmartLearnUni&#34;, funded by the Spanish Ministry of Science and Innovation 2020 I+D+i Projects - RTI Type B. Reference: PID2020-117111RB-I00. The authors also gratefully acknowledge the collaboration of the health sciences students at the University of Burgos, especially the students studying for their Occupational Therapy and Nursing degrees.

This protocol was executed in compliance with the procedural regulations of the Bioethics Committee of the University of Burgos (Spain) number IO 03/2022. Before participating, the respondents were made fully aware of the research objectives and provided their informed consent. They received no financial compensation for their participation.
1. Participant recruitment
<ol>
	<li>Recruit adult participants aged between 20 years old and 56 years old from two groups (students and teachers) in higher education, specifically undergraduate students in occupational therapy. 
	NOTE: Table 1 shows the list of students disaggregated by teaching modality and by age and gender. The PBL methodology was used at two different periods during the COVID-19 pandemic: the state of emergency with full confinement (teaching had to be via e-Learning due to total confinement) and the state of emergency with partial confinement (only students who tested positive for COVID-19 or who had been in direct contact with someone testing positive were confined; in this case, b-Learning was applied).</li>
</ol>
Table 1: Sample characteristics. Sample characteristics (age, gender) in both groups (e-Learning teaching method vs. b-Learning teaching method). Abbreviations: e-Learning = online classes; b-Learning = blended classes (online and physical classes); M age =Mean age; SD age=Standard deviation age. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_1.xls" target="_blank">Please click here to download this Table.</a>
2. Experimental procedure
<ol>
	<li>Session 1: Collecting consents and briefing the participants
	<ol>
		<li>Collect informed consent, personal data, and background knowledge.</li>
		<li>During the first week of teaching, inform the trainees about the objectives of the study and about data collection, processing, and storage. If the students agree to participate, ask them to sign an informed consent form. 
		NOTE: Participation in this study was voluntary, and there was no financial compensation. This aspect ensures motivation toward the execution of the task free of economic conditioning factors.</li>
	</ol>
	</li>
	<li>Session 2: Assessing the students&#39; prior knowledge
	<ol>
		<li>During the first week of teaching, ask the students to complete a questionnaire about their prior knowledge of the core subject concepts. Record the answers to the closed questions in a dichotomous manner or on a Likert-type scale from 1 to 5 (Table 2). 
		NOTE: The prior knowledge questionnaire contains closed questions related to the methodology to be applied in the subject, which, in this case, includes personalized voice assistants to report course events, process-oriented automatic feedback&#160;systems, virtual laboratories, flipped classroom&#160;experiences, and PBL. The questionnaire was applied anonymously through the UBUVirtual learning platform, which is a Moodle environment (Modular Object-Oriented Dynamic Learning Environment) and was filled in by the students online. The questionnaire in this study had a Cronbach&#39;s alpha reliability index of &#945; = 0.88 for the overall scale and &#945; = 0.87-0.89 for each of the items if the item was removed. The questionnaire also includes an open-ended question about the student&#39;s motivation toward the subject.</li>
	</ol>
	</li>
	<li>Session 3: Informing the students about the project-based teaching methodology and the different resources to be used
	<ol>
		<li>Provide the students with a guide for how PBL will be delivered as well as rubrics for the evaluation of the project and the presentation of the project.</li>
		<li>Inform the students about the advanced learning technology&#160;resources available (intelligent voice assistants, process-oriented personalized feedback, virtual laboratories, questionnaires with feedbackon the qualification, flipped classroom experiences,and PBL experiences). 
		NOTE: Students have access to a document detailing the PBL methodology (see Supplemental Material Appendix 1) and two rubrics for carrying out the project and presenting it (see Supplemental Material Appendix 2).
		<ol>
			<li>Ensure that, in the e-Learning group, students are assisted by an intelligent voice assistant that informs them about course events.</li>
			<li>In the b-Learning group, do not use this resource, but ask the participants to carry out the project in a physical simulation laboratory and not only virtually, as in the case of the e-learning group.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Session 4: Choice of case study
	<ol>
		<li>Organize the students into groups of two to five participants and ask each group to choose from a series of practical cases related to different developmental disorders (physical, psychological, or sensory) in children aged 0-6 years. 
		NOTE: The case studies include data from the clinical history (an example can be found in the Supplemental Material Appendix 3).</li>
	</ol>
	</li>
	<li>Session 5: Description of the scope of the project intervention
	<ol>
		<li>Ask each group to prepare an introduction describing the type of service in which they will implement their project (they can choose between an intervention in the field of health, education, patient groups, or private services within the framework of early care). 
		NOTE: The students elaborate the works in a creative way; some examples can be consulted in the Supplemental Material Appendix 4.</li>
	</ol>
	</li>
	<li>Session 6: Description of the professionals working in the intervention area
	<ol>
		<li>Ask each group to describe the role of the professionals who will intervene, as well as the relationship structure that will be applied, encouraging interdisciplinary working.</li>
	</ol>
	</li>
	<li>Session 7: Description of the intervention case study
	<ol>
		<li>Ask each group to describe the characteristics of the pathology or developmental disorder.</li>
	</ol>
	</li>
	<li>Session 8: Creation of the intervention program
	<ol>
		<li>Ask each group to prepare various phases within the intervention program that address the following elements: an initial user assessment, the proposed evaluation indicators based on the results from the initial assessment, the proposed intervention procedure to achieve the development of skills or behaviors in children, the materials required for the intervention, a proposal of the generalization activities, and a plan for the follow-up for the intervention.
		<ol>
			<li>In virtual laboratories, have the teacher help the students in the e-Learning group. 
			NOTE. For an example of the help provided to the e-Learning students in virtual laboratories, see Supplemental Material Appendix 5.</li>
			<li>Carry out this part of the project via practical classes in the simulation center for the b-Learning group.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Session 9: Production of a project document
	<ol>
		<li>Ask each group to provide a document explaining the project created for the practical case chosen. 
		NOTE: The project is evaluated using the project development evaluation rubric (see Supplemental Material Appendix 2).</li>
	</ol>
	</li>
	<li>Session 10: Presentation of the project
	<ol>
		<li>Ask each group to present the project about the practical case they chose. 
		NOTE: The presentation is given to their classmates and the professor.</li>
		<li>Evaluate the presentation. Perform the evaluation of the presentation according to the presentation evaluation rubric (see Supplemental Material Appendix 2).</li>
	</ol>
	</li>
	<li>Session 11: Follow-up of the students&#39; work
	<ol>
		<li>Assess the interaction of the student groups on the UBUVirtual platform via the students&#39; behavior monitoring software41 tool, which allows the interaction of students to be analyzed individually (Figure 2) and in groups (Figure 3).</li>
		<li>To perform group analysis: Click the Group Icon &#62; Group 01 &#62; Select All &#62; Logs. Select Components &#62; Select All pointing from Visual Analytics and then select Heatmap. This will generate a heatmap that allows the interaction to be analyzed individually, as shown in Figure 2.</li>
		<li>To perform the individual analysis: Click Select All &#62; Comparisons. Select Logs &#62; Components. Then, click Select All pointing from Visual Analytics and select Heatmap. This will generate a heatmap that allows the interaction to be analyzed as a group, as shown in Figure 3.</li>
		<li>Use the tool to determine what interactions each student has individually (Figure 4) and within a group (follow step 2.11.2 and step 2.11.3).</li>
	</ol>
	</li>
	<li>Session 12: Evaluation of student satisfaction with project-based learning
	<ol>
		<li>Ask each student to complete an opinion survey about their satisfaction with PBL work at the end of the course (Table 3). 
		NOTE: The PBL satisfaction survey consists of 17 closed response questions measured on a Likert-type scale from 1 to 5 and two open response questions. The Cronbach&#39;s alpha reliability index in this study was &#945; = 0.89 for the overall scale and &#945; = 0.88-0.91 for each item if the item was removed.</li>
	</ol>
	</li>
	<li>Session 13: Data analysis
	<ol>
		<li>For the first question (RQ1: Are there significant differences in the learning outcomes and satisfaction of health sciences students studying occupational therapy depending on whether the PBL methodology is implemented via e-Learning versus b-Learning, considering the effects of students&#39; prior knowledge?), import all the data from the spreadsheet into the statistical program, SPSS42: elaboration of PBL (LOEPBL), presentation of PBL (LOEXPBL), learning outcomes total (LOT), obtaining LMS access (LMSA), satisfaction of the students with teaching (SPBL), the independent variable (the type of e-Learning modality, i.e., implementation year of the teaching, and the covariate (prior knowledge).
		<ol>
			<li>Do this for both the groups of e-Learning versus b-Learning teaching modalities (during the first year of the COVID-19 pandemic with online teaching) and for b-Learning (during the second year of the COVID-19 pandemic with mixed teaching, partly in person and partly online).</li>
			<li>Perform an ANCOVA with fixed effects (teaching modality: e-Learning vs. b-Learning) and the covariate (prior knowledge).</li>
			<li>Select Multivariate Analysis and include the dependent variables (LOEPBL, LOEXPBL, LOT, LMSA, and SPBL), the independent variable (the type of teaching modality for b-Learning), and the covariate (prior knowledge).</li>
			<li>Estimate the marginal means and perform the ANCOVA analysis by pressing the OK button.</li>
		</ol>
		</li>
		<li>For the second question (RQ2: Do the clusters of participants found match the learning outcomes, learning behaviors, and perceived satisfaction as a function of teaching modality [e-Learning vs. b-Learning]?), perform the following steps:
		<ol>
			<li>Select Means cluster analysis and then the variables LOEPBL, LOEXPBL, LOT, LMSA, and SPBL. Then, select Cluster Membership then pause very slightly, click Continuous and pause very slightly again. Select Initial cluster centers &#62; ANOVA table &#62; cluster information for each case. Then, select Continue.</li>
			<li>Select the cross table between the data found for cluster membership and the e-Learning versus b-Learning teaching modality groups. For Row, select the Variable Year (the type of e-Learning modality, i.e., teaching implementation year). For Column, select the Cluster Number of the case.</li>
			<li>Perform a cluster analysis using visualizing software. To do so, import the data into the visualizing software and select Heat Map &#62; Radviz. Implement the visualization of the clusters with the different variables studied with respect to the relationship with the teaching modality used. 
			NOTE: The free data mining software43 (see the Table of Materials) was used for this analysis.</li>
		</ol>
		</li>
		<li>For the third question (RQ3: Are the students&#39; suggestions for the improvement of the PBL methodology different depending on the teaching modality, e-Learning versus b-Learning?), perform the following steps.
		<ol>
			<li>Perform a qualitative analysis of the open answers found in the PBL13 satisfaction scale for the two groups of e-Learning versus b-Learning using qualitative data analysis software44. Import the answers to the open-ended questions obtained on the satisfaction of the students with the teaching (SPBL) into the software.</li>
			<li>Select Categorizing Student Responses in the two teaching modalities, e-Learning versus b-Learning.</li>
			<li>Select the Document group (teaching modality: e-Learning vs. b-Learning) analysis.</li>
			<li>Select Sankey Diagram.</li>
			<li>Export the results into a spreadsheet software. 
			NOTE: First, the responses to the open-ended questions were categorized in each teaching modality group (e-Learning vs. b-Learning), and a frequency analysis was performed on the responses by group and categorization. The results were visualized using a Sankey diagram. This was done using qualitative data analysis software44 (see the Table of Materials)</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
Table 2: Prior knowledge questionnaire13. Open and closed questions from the prior knowledge questionnaire. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_2.xls" target="_blank">Please click here to download this Table.</a>
Table 3: Scale of satisfaction with PBL13. Open and closed questions from the PBL satisfaction scale. Abbreviation: PBL = project-based learning. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_3.xls" target="_blank">Please click here to download this Table.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63601/63601fig02.jpg" /> 
Figure 2: Analysis of a single student&#39;s behavior on the UBUVirtual platform. Heat map of the behaviors carried out by the student on the virtual learning platform. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/63601fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63601/63601fig03.jpg" /> 
Figure 3: Analysis of all student behaviors on the UBUVirtual platform. Analysis of the learning behaviors performed by the students on the virtual learning platform with respect to task completion. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/63601fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63601/63601fig04.jpg" /> 
Figure 4: Analysis of student behavior in a group on the UBUVirtual platform. Analysis of the learning behaviors performed by the students in work groups on the virtual learning platform with respect to the completion of tasks. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/63601fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

The authorizations for recording the images and videos presented in this work are attached.
A model of a practical assumption that is applied in the work with PBL is also attached (see Supplementary Material Appendix 3: An example of a case study). The authors declare that they have no competing financial interests.

This study leads to the conclusion that the teaching modality (e-Learning vs. b-Learning) may affect the results in different elements of PBL17,18. In future studies, this aspect will be explored in greater depth to see if the same pattern is found with students from other (particularly health sciences) courses, since this is the subject of this protocol. In contrast, no differences were found in the total learning outcomes or in student satisfaction with the teaching methodology. This is in line with results from other studies4,13,14,15,18,29.
In summary, the use of a PBL teaching methodology has been shown to be effective both for students&#39; learning outcomes and their motivation for learning, which is consistent with the findings from other studies2,3,4. However, the students in the b-Learning group expressed an interest in more in-person&#160;teaching. This aspect supports a hybrid use of the methodology8. Similarly, the students in both groups appreciated that this methodology guides them toward the practical aspects of their profession4,13. It was also found that the social situation in which the teaching was delivered affected the students&#39; satisfaction and critical reflections about their learning. The learning was more participative in the second part of the pandemic, where there were greater possibilities for in-person learning4,14,15,18,29. Not all students were found to respond in the same way or experience the same amount of participation22. This is an important aspect to be considered by teachers with respect to using this methodology, and supporting the methodology requires teachers with digital competencies along with the use of EDM techniques32,33,36 and the analysis of students&#39; open responses using mixed techniques39,40,41.
Finally, one limitation of this study is related to the PBL methodology, which was used only with students studying for a single degree-occupational therapy. The methodology was not implemented with multi- or interdisciplinary teaching designs31.

Characteristics of the project-based learning methodology 
Nowadays, professionals in various sectors face numerous (technical, sociopolitical, and economic) challenges arising from globalization in a variety of spheres, such as the environment1. These issues influence the global spread of diseases, thus diminishing resources, increasing poverty, and necessitating the responsibility to create sustainable societies1. Health experts have the potential and the skills to ensure the viability of communities by advancing care strategies and techniques on a large scale while contributing to the improvement of people&#39;s quality of life2. The United Nations has highlighted the need to address these challenges with the active participation of all countries in its Sustainable Development Goals (SDGs) 1. Health professionals&#39; activities can be linked to many of the proposed goals. Therefore, those who train healthcare students must acquire pedagogical approaches that help students to gain the competencies that will allow them to face these new challenges1,2.
Among the various existing pedagogical methodologies, project-based learning (PBL)3 stands out as a methodology that helps students develop the competencies they will need to safely and effectively deal with the future challenges of their professions3. PBL is an innovative pedagogical approach that overcomes the limitations of traditional methodologies by transforming the teaching-learning process to make students the protagonists of their own learning. This change helps students acquire problem-solving skills in real-life contexts4. PBL is based on the teacher producing research questions5 that are included in practical scenarios related to the discipline. The students have to solve the problems in collaborative groups. The final objective is the acquisition of knowledge (conceptual, procedural, and attitudinal) through group work in the resolution of the task or problem6.
This approach originated in the fields of education and constructivist psychology, and from those beginnings, it has been adapted to different disciplines6. However, implementing this methodology can be affected by a lack of educational resources and infrastructure, and because of this, a hybrid model is sometimes used that combines traditional teaching with PBL7,8. However, these models have exhibited limitations in the learning experience1. For this reason, it is important to develop pedagogical protocols that guide and facilitate the use and interpretation of these methodologies. In addition, in recent years, the PBL methodology has been implemented through virtual learning platforms - Learning Management Systems (LMS)9&#160;- in what is called Online Project-Based Learning (OPBL)10.
Project-based learning methodology for university students in the health sciences field 
The protocol is important for helping teachers to apply these methodologies and interpret the results arising from the teaching and learning process9. The protocol can be used from the beginning of schooling to higher education and can be tailored to different mental, physical, and socioeconomic conditions11. The presented protocol focuses on working with university students in the health sciences. The teaching model is based on organizing learning around projects prepared by the teacher, who serves as an initial stimulus, and from this framework, the student becomes the main focus of their own learning11. The approach is an active, comprehensive, and constructive way for students to acquire basic competencies (conceptual, procedural, and attitudinal) that are closely related to their future professional practice4,12.
Learning methods are combined to stimulate the acquisition of cognitive and metacognitive competencies related to conceptual and procedural knowledge4,13,14, all of which contribute to students developing critical thinking, increased motivation toward learning, and autonomy in decision-making and problem-solving13. However, one of the keys to the successful application of this methodology is that the projects the teacher prepares must be directly related to real practice, and the students must be given autonomy in how they approach the problems based on minimum initial guidelines4,9. This means that the teachers need to clearly define the tools for evaluating the students&#39; competencies and to give them guidance about the evaluation criteria that will be used to shape the PBL, as well as to assess how well the evaluation is done15,16. Furthermore, students benefit from collaborating with their peers to learn to argue and be assertive through debate, thus developing communication and teamwork skills4,16.
The main aim for university students is to &#34;learn to learn&#34; by dealing with the challenges the teacher sets to facilitate the acquisition of these competencies14 (which will later be implemented in the future working lives of the students). The results of using this methodology indicate that it encourages autonomy in learning and solving practical problems4. An added advantage of this methodology is that it is very effective in helping students acquire competencies, specifically in health sciences, in online&#160;teaching and blended-Learning (b-Learning)17,18,19. This has become especially important due to the COVID-19 pandemic20,21.
The common elements of PBL can be summed up as follows: (1) first, the concepts related to the project are explained to the students (in online&#160;teaching environments, flipped learning&#160;experiences can be used); (2) the project plan is defined and analyzed; (3) a review of the supporting theory is carried out, and a plan is drawn up to help understand the object of study; (4) objectives and questions are proposed for addressing the problem; (5) possible solutions are formulated, and the most viable options are evaluated; (6) these solutions are implemented; (7) the results are reported; (8) critical reflection is conducted, feedback is provided, and the process is evaluated, including suggesting new questions; and (9) the process is finished by presenting the work4,6,13.
It should also be kept in mind that not all students respond in the same way to this type of methodology. Students who are more actively involved in their learning, those who are more critical thinkers, and those who have better interpersonal relationship skills tend to achieve better results22. It is also possible that students who are used to traditional learning methodologies may feel frustrated. This is why students should be given clear working rules and a timetable for the implementation of the project phases from the beginning8. As noted above, PBL has been widely used within health sciences degrees, and there is extensive evidence supporting it in the literature18,23,24. However, some aspects that could be improved have been identified, which are related to clinical skills seminars and the dissemination of experience in the scientific community25,26.
This methodology seems to have better results for degrees with a higher proportion of internships (for example, nursing vs. medicine, occupational therapy versus psychology or nutrition)3. In addition, recent studies have suggested implementing PBL methodology through joint training projects between different degrees to work on interaction in real contexts. The objective would be the acquisition of multidisciplinary and interdisciplinary competencies27,28. However, to implement this type of methodology well, the teaching staff must be qualified to implement it, and the students must develop critical and divergent thinking with respect to clinical practice, especially in the case of health sciences3,15,18,29.
Another important aspect of the implementation is the need to evaluate student satisfaction with the PBL methodology training and analyze their ideas for improvement19,30,31. More specifically, it has been reported that occupational therapy students have found this methodology essential for their learning as it allows them to learn how to solve similar problems to those they will face in their professional lives, which enhances their motivation31. Similarly, a longitudinal study that began in 1993-in which a hybrid model of PBL was evaluated in occupational therapy students-demonstrated that the students had very positive opinions of group learning for their future professional practice7. Another study on occupational therapy students found a high degree of satisfaction with the methodology. This is because students think that this way of learning helps them acquire knowledge through practice and enables them to respond appropriately in their future work13.
In summary, using the PBL methodology in health sciences courses is key to students&#39; acquisition of professional competencies. Various studies have indicated the steps to follow to prioritize the interventions and the design of the materials to achieve effective, satisfactory learning. Therefore, it is very important to monitor students&#39; learning processes to detect early or potential problems with learning and address them. Educational Data Mining (EDM) and mixed-methods analysis techniques should be used for monitoring, the essential fundamentals of which are discussed below.
Educational Data Mining techniques 
When LMSs are used in either e-Learning or b-Learning teaching models, a series of logs or log files are generated that capture each of the student and teacher interactions. These logs are evidence of the learning behaviors and can be analyzed using EDM or Learning Analytics&#160;(LA)32 techniques. These techniques allow the teacher to learn what student interaction exists, how much interaction exists, and what is the quality of the interaction33. In particular, EDM techniques make it easier to discover students&#39; learning behavior patterns and their interactions4. EDM techniques can be used to study different profiles35,36 (oriented toward students or oriented toward educators).
The final objective is to provide feedback for the instruction, evaluate the course content structure, analyze elements that have been effective in the learning processes, classify the type of students, and identify needs for the guidance and monitoring of learning. This helps in determining each student&#39;s most common learning patterns and the frequency of errors, which can then be used to tailor the most appropriate educational responses. One of the things that EDM techniques can be used for is monitoring students&#39; learning processes to offer appropriate help via mentoring. EDM techniques include supervised learning techniques (prediction or classification techniques)37 and unsupervised learning techniques36 (clustering techniques)38. Using EDM techniques in teaching processes that include active teaching methodologies, such as PBL, has been shown to be very effective for studying individual student behavior and student behavior in collaborative groups10.
The application of computer-assisted qualitative analysis techniques 
In the last two decades, quantitative and qualitative techniques, or a combination of the two, have been applied in research in what has been called mixed methods39. The use of mixed methods for data analysis is especially useful when dealing with complex problems of analysis, such as monitoring students&#39; learning behaviors. The use of these methods allows for the transformation of qualitative data, such as student feedback from open-ended questions in a survey, into qualitative data, and the analysis of the data with different data visualization techniques40. A variety of computer-assisted (or -aided) qualitative data analysis software (CAQDAS) can be used to facilitate data transformation and analysis41.
A summary of the procedure for teaching through the PBL methodology, the analysis of learning behaviors, the use of EDM, and computer-based qualitative analysis techniques is shown in Figure 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63601/63601fig01.jpg" /> 
Figure 1: PBL work and the use of EDM and computer-based qualitative analysis techniques. Data collection and processing applying EDM and text mining techniques in e-Learning and b-Learning teaching environments. Abbreviations: PBL = project-based learning; EDM = Educational Data Mining; DB = database; e-Learning = online classes; b-Learning = blended classes (online and physical classes). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/63601fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Three research questions were posed in this study: RQ1: Are there significant differences in the learning outcomes and satisfaction of health sciences students studying occupational therapy depending on whether the PBL methodology is implemented via e-Learning versus b-Learning, considering the effects of students&#39; prior knowledge? RQ2: Do the clusters of participants found match the learning outcomes, learning behaviors, and perceived satisfaction as a function of teaching modality (e-Learning vs. b-Learning)? RQ3: Are the students&#39; suggestions for the improvement of the PBL methodology different depending on the teaching modality, e-Learning versus b-Learning?
The following protocol can be used by teachers in healthcare and can also be modified to work with students in other knowledge areas.

<table><tbody><tr><td>Atlas.ti v.9</td><td>Atlas.ti</td><td></td><td></td></tr><tr><td>Orange v. 3.30</td><td>Orange</td><td></td><td></td></tr><tr><td>SPSS v.24</td><td>SPSS</td><td></td><td></td></tr><tr><td>UBUVirtual</td><td>UBU</td><td></td><td></td></tr></tbody></table>

project-based learning guidelines for health sciences students: an analysis with data mining and qualitative techniques

Academic leaders all over the world are encouraging the use of active methodologies in teaching, especially in higher education. The reason for this is that social changes are happening at an ever-increasing rate, and they require students and teachers to develop digital skills. This is especially significant for health sciences degrees, in which future graduates must have effective problem-solving skills. To respond to this challenge, the use of a project-based learning (PBL) methodology, together with various monitoring techniques based on the use of Educational Data Mining (EDM) and mixed methods, will provide teachers with information about the effectiveness of the methodology and guide the implementation of personalized educational responses.
This study provides a protocol for the application of the PBL methodology in e-Learning and blended-Learning (b-Learning) teaching modalities for health sciences students studying occupational therapy in higher education. In addition, statistical techniques for the analysis of covariance and unsupervised learning allow differences to be detected between the two teaching modalities, thus specifying their effectiveness in terms of a range of variables related to behavioral patterns, performance, and satisfaction. Data visualization also helps in understanding the qualitative aspects of the learning process. These data will help teachers to produce more effective proposals for the implementation of the PBL methodology based on the based on the context of the teaching-learning process. Therefore, this protocol offers many resources and materials to help teachers implement the PBL methodology in e-Learning and b-Learning teaching methods.

The 98 participants recruited for the present study were undergraduate occupational therapy students aged between 21 years old and 56 years old (Table 1). The protocol was tested over 2 academic years at the University of Burgos. The delivery of the protocol is summarized in Table 4.
Table 4: Summary of the application of the protocol for project-based learning for university students. Abbreviation: LMS = Learning Management Systems. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_4.xls" target="_blank">Please click here to download this Table.</a>
The first research question (RQ1) was as follows: are there significant differences in the learning outcomes and satisfaction of health sciences students studying occupational therapy depending on whether the PBL methodology is implemented via e-Learning versus b-Learning, considering the effects of students&#39; prior knowledge?
As Table 5 shows, there were significant differences in the elaboration (LOEPBL) scores, with the e-Learning group scoring higher. The b-Learning group scored higher in presentation (LOEXPBL) and LMS access (LMSA). No significant differences were found in the total learning outcomes (LOT), and no effect was found for prior knowledge as the covariate.
Table 5: ANCOVA of a fixed effects factor (teaching modality, e-Learning vs. b-Learning), the covariate (level of prior knowledge), and the effect value, &#951;2. ANCOVA of a fixed effects factor (teaching modality, e-Learning vs. b-Learning), the covariate (level of prior knowledge), and the effect value, &#951;2. Abbreviations: PBL = project-based learning; LOEPBL = elaboration of PBL; LOEXPBL = presentation of PBL; LOT = total learning outcomes; LMS = Learning Management Systems; LMSA = LMS access; SPBL = satisfaction with PBL; e-Learning = online classes; b-Learning = blended classes (online and physical classes); M = mean; SD = standard deviation; ANCOVA = analysis of covariance. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_5.xls" target="_blank">Please click here to download this Table.</a>
The second research question (RQ2) was as follows: do the clusters of participants found match the learning outcomes, learning behaviors, and perceived satisfaction as a function of teaching modality (e-Learning vs. b-Learning)?
Two clusters were found in which differences were detected in the different variables, but it could not be established that one was superior to the other in all the variables (Table 6).
Table 6: Final cluster centers. Abbreviations: PBL = project-based learning; LOEPBL = elaboration of PBL; LOEXPBL = presentation of PBL; LOT = total learning outcomes; LMS = Learning Management Systems; LMSA = LMS access; SPBL = satisfaction with PBL. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_6.xls" target="_blank">Please click here to download this Table.</a>
Subsequently, a cross table was prepared between the values of the clusters of belonging assigned to each participant with respect to the teaching modality variable (e-Learning vs. b-Learning) (Table 6) and the percentage of belonging to each of the groups. A contingency coefficient of C = 0.40, p &#60; 0.001, was obtained (Table 7).
Table 7: Cross-tabulation of participant by cluster number Abbreviations: e-Learning = online classes; b-Learning = blended classes (online and physical classes). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_7.xls" target="_blank">Please click here to download this Table.</a>
In addition, an ANOVA was performed between clusters for the variables of elaboration (LOEPBL), presentation (LOEXPBL), total learning outcomes (LOT), LMS access (LMSA), and students&#39; satisfaction with teaching (SLPBL). Significant differences were found in presentation (LOEXPBL, p = 0.03) and LMS access (LMSA, p &#60; 0.001) (see Table 8).
Table 8: ANOVA clusters. Abbreviations: PBL = project-based learning; LOEPBL = elaboration of PBL; LOEXPBL = presentation of PBL; LOT = total learning outcomes; LMS = Learning Management Systems; LMSA = LMS access; SPBL = satisfaction with PBL; df = degrees of freedom; ANOVA = analysis of variance. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_8.xls" target="_blank">Please click here to download this Table.</a>
A visual cluster analysis was performed by means of the k-means technique using visualization software43, taking teaching modality, e-Learning versus b-Learning, as a variable with respect to the different variables studied: elaboration (LOEPBL), presentation (LOEXPBL), total learning outcomes (LOT), access to the LMS (LMSA), and student satisfaction with teaching (SPBL) (Figure 5).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63601/63601fig5v2.jpg" /> 
Figure 5: Cluster analysis of the variables LOEPBL, LOEXPBL, LOT, LMSA, and SPBL with respect to the variable &#34;Teaching modality, e-Learning versus b-Learning&#34;. Visualization of the clusters found with respect to the variables LOEPBL, LOEXPBL, LOT, LMSA, and SPBL. Abbreviations: PBL = project-based learning; LOEPBL = elaboration of PBL; LOEXPBL = presentation of PBL; LOT = total learning outcomes; LMS = Learning Management Systems; LMSA = LMS access; SPBL = satisfaction with PBL; e-Learning = online classes; b-Learning = blended classes (online and physical classes). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/63601fig5v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Next, a heat map was constructed for the behavior of the variables in the clusters. Greater discrimination was found in cluster 1 (the students included in this cluster had less access to the virtual learning platform) and cluster 2 (the students included in this cluster had more access to the virtual learning platform) in the behaviors of students on the virtual learning platform (UBUVirtual is a virtual learning platform based on Moodle) and in the type of teaching (e-Learning vs. b-Learning) (Figure 6).
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63601/63601fig06.jpg" /> 
Figure 6: Heat map visualization of the clusters in the variables LOEPBL, LOEXPBL, LOT, LMSA, and SPBL. Abbreviations: PBL = project-based learning; LOEPBL = elaboration of PBL; LOEXPBL = presentation of PBL; LOT = total learning outcomes; LMS = Learning Management Systems; LMSA = LMS access; SPBL = satisfaction with PBL. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/63601fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The third research question (RQ3) was as follows: are the students&#39; suggestions for the improvement of the PBL methodology different depending on the teaching modality (e-Learning vs. b-Learning)?
The answers were categorized into two groups, the e-Learning versus b-Learning groups, using the qualitative data analysis software44. The frequencies by categorization code are shown in Table 9, and Figure 7 shows an analysis of the students&#39; responses in the two groups by applying a Sankey diagram.
Table 9: Frequency analysis of the categorized responses of the students in the two intervention groups (Group 1, e-Learning teaching, and Group 2, b-Learning teaching). Abbreviations: e-Learning = online classes; b-Learning = blended classes (online and physical classes). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/JoVE_Table_9.xls" target="_blank">Please click here to download this Table.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/63601/63601fig07.jpg" /> 
Figure 7: Sankey plot of the categorized responses in the two intervention groups (Group 1, e-Learning teaching, and Group 2, b-Learning teaching). Sankey plot of the categorized responses in the two intervention groups. Abbreviations: e-Learning = online classes; b-Learning = blended classes (online and physical classes). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/63601fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
How do these results indicate that the suggestions for the improvement of the teaching were different depending on whether the teaching was via e-Learning or b-Learning? The students in both groups agreed that the teaching had been satisfactory. Nonetheless, the b-Learning group noted the importance of using the simulation center and their desire for real practice with children, thereby increasing the teaching in classroom practice. The teaching modality used (e-Learning vs. b-Learning) led to differences in the results of applying the PBL methodology.
More specifically, the students in the e-Learning group scored higher in the execution of the project, while the b-Learning group scored higher in presenting the project. There was also increased access to the virtual platform in the b-Learning group. Two clusters were found, with cluster 2 showing better results in all the variables studied except for two (total learning outcomes, for which the results were the same between the two groups, and satisfaction with the teaching methodology, for which the results were very similar in both groups).
Similarly, in the e-Learning group, 82.6% of the students were in cluster 1, and 17.39% were in cluster 2. In the b-Learning group, 59.62% were in cluster 2, and 40.38% were in cluster 1. Therefore, it can be concluded that the teaching modality used seems to have an effect on the student learning outcomes and student activity in the LMS, with b-Learning being more effective than e-Learning in project presentation and student activity in the LMS. In contrast, e-Learning seems to be more effective with regard to creating the project. Nonetheless, the PBL methodology is very effective in both cases since the means of the final learning outcomes were high in both groups with very low variability (e-Learning mean = 8.89, standard deviation = 0.48; b-Learning mean = 8.65, standard deviation = 0.92). In addition, satisfaction was very high in both modalities, with no significant differences between the groups.
These results support the first question from RQ1 and indicate the directions for future research to examine these aspects more deeply. The differences noted above can also be seen by analyzing the qualitative data regarding student satisfaction within the two teaching modalities. It is important to remember that the e-Learning was delivered at the most critical point in the state of emergency due to COVID-19, during which neither in-person nor blended teaching could be carried out. The students&#39; evaluations of how the PBL methodology was implemented in the online mode showed that it was very satisfactory, as it allowed them to continue their training in a way that was close to normal and to relate it to their professional practice. This may be why the e-Learning group did not make many suggestions for improvements. In contrast, the b-Learning group, who were taught during the second year of the pandemic, felt that the PBL methodology imparted via the LMS and in the face-to-face simulation center was beneficial for their training. However, this group suggested many more ideas for improvements to the teaching, which were mainly focused on increasing the in-person classes, specifically in terms of practical activities with real users.
Supplemental Material Appendix 1. This includes Table 1 and Table 2.&#160;(Table 1) Document including the relationship between metacognitive strategies, sub-strategies, and the activities to develop them. Project-based learning is described from the current state of the field. In addition, information is presented on the relationship between the steps of PBL and the development of different metacognitive strategies. This is a very useful document for teachers who want to apply this educational methodology. (Table 2) Rubric for the evaluation of the project resolution process. A rubric is presented for monitoring the project development process with respect to the use of different metacognitive strategies. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/Supplentary_Materials_Appendix_1.docx" target="_blank">Please click here to download this File.</a>
Supplemental Material Appendix 2: This includes Table 1 and Table 2.&#160;(Table 1) Rubric for the evaluation of the presentation of the PBL. A rubric is presented for the evaluation of the project development process with respect to the presentation and defense of the project.&#160;(Table 2)&#160;Rubric for the evaluation of the elaboration of the PBL. A rubric is presented for the assessment of the project development process with respect to project evaluation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/Supplementary material Appendix 2.docx" target="_blank">Please click here to download this File.</a>
Supplementary Material Appendix 3: An example of a case study. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/supplementary material Appendix_3.docx" target="_blank">Please click here to download this File.</a>
Supplementary Material Appendix 4: Examples of work produced by the students. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/Supplentary_Materials_Appendix_4.docx" target="_blank">Please click here to download this File.</a>
Supplementary Material Appendix 5: Examples of the virtual laboratories developed. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63601/Supplentary_Materials_Appendix_5.docx" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques at JoVE.com

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Here, we present a protocol for the behavioral analysis of a project-based learning methodology for health sciences students (20-56 years old). The protocol facilitates the comparison of the participants' performance in e-Learning versus blended-Learning (b-Learning) through a monitoring tool. The results are analyzed using Educational Data Mining and qualitative techniques.

Academic leaders all over the world are encouraging the use of active methodologies in teaching, especially in higher education. The reason for this is ...

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SCIENTISTS IN ACTION

3.4K Views.  University of Burgos. Inleiding.Hier presenteren we een protocol voor de gedragsanalyse van projectgebaseerde leermethodologie voor studenten gezondheidswetenschappen van 20 tot 56 jaar oud. Het protocol vergemakkelijkt de vergelijking van de prestaties van de deelnemers in e-learning versus b-learning door middel van een monitoringtool. De resultaten worden geanalyseerd met behulp van educatieve datamining en kwalitatieve technieken.Onderzoeksvragen. Onderzoeksvraag één, zullen er significante verschille...

Video: Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Using Learning Outcome Measures to assess Doctoral Nursing Education

Education programs at all levels must be able to demonstrate successful program outcomes. Grades alone do not represent a comprehensive measurement methodology for assessing student learning outcomes at either the course or program level. The development and application of assessment rubrics provides an unequivocal measurement methodology to ensure a quality learning experience by providing a foundation for improvement based on qualitative and quantitatively measurable, aggregate course and program outcomes.  Learning outcomes are the embodiment of the total learning experience and should incorporate assessment of both qualitative and quantitative program outcomes. The assessment of qualitative measures represents a challenge for educators in any level of a learning program. Nursing provides a unique challenge and opportunity as it is the application of science through the art of caring. Quantification of desired student learning outcomes may be enhanced through the development of assessment rubrics designed to measure quantitative and qualitative aspects of the nursing education and learning process. They provide a mechanism for uniform assessment by nursing faculty of concepts and constructs that are otherwise difficult to describe and measure. A protocol is presented and applied to a doctoral nursing education program with recommendations for application and transformation of the assessment rubric to other education programs. Through application of these specially designed rubrics, all aspects of an education program can be adequately assessed to provide information for program assessment that facilitates the closure of the gap between desired and actual student learning outcomes for any desired educational competency.

The structure and process for measuring student learning outcomes through customizable assessment rubrics is discussed and applied to a doctoral nursing education program.

Education programs at all levels must be able to demonstrate successful program outcomes. Grades alone do not represent a comprehensive measurement ...

Medicine

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity

Typically, self-reports are used in educational research to assess student response and performance to a classroom activity. Yet, addition of biological and physiological measures such as salivary biomarkers and galvanic skin responses are rarely included, limiting the wealth of information that can be obtained to better understand student performance. A laboratory protocol to study undergraduate students&#39; responses to classroom events (e.g., exams) is presented. Participants were asked to complete a representative exam for their degree. Before and after the laboratory exam session, students completed an academic achievement emotions self-report and an interview that paralleled these questions when participants wore a galvanic skin sensor and salivary biomarkers were collected. Data collected from the three methods resulted in greater depth of information about students&#39; performance when compared to the self-report. The work can expand educational research capabilities through more comprehensive methods for obtaining nearer to real-time student responses to an examination activity.

Research on undergraduate students' academic achievement emotions have primarily relied on self-reports in laboratory settings. Studies rarely include bio-physiological measures to these self-reports. This protocol will describe a methodology that integrates self-reports with and bio-physiological measures to assess student response and performance during a laboratory examination activity.

Typically, self-reports are used in educational research to assess student response and performance to a classroom activity. Yet, addition of biological ...

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -&#62; BAPS1 -&#62; ST -&#62; cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

Genetics

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of&#160;unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous ...

Implementing Gibson Assembly to Enhance Molecular Biology Education and Skills

Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience

The continuous advancement of molecular biology techniques requires that molecular biology curricula are&#160;regularly refined to effectively prepare students to enter the workforce with modern competencies. In particular, the emergence of Gibson Assembly, a highly customizable and adaptive molecular cloning technique, has advanced the landscape of molecular cloning in numerous research environments. Thus, we created a Gibson Assembly cloning module for deployment in a molecular biology laboratory course at California Polytechnic State University, San Luis Obispo and evaluated student learning outcomes from the module. Over three iterations of the course, students participated in an experiment-based independent project that involved cloning three unique plasmid libraries to support research projects in natural products biosynthesis. Students were given pre- and post-questionnaires to evaluate their understanding of molecular cloning and their confidence in molecular biology terms and techniques. Students&#8217; responses showed a significant increase in both learning molecular cloning concepts and in self-reported confidence with molecular cloning terms and techniques. This module framework can be generalized to teach Gibson Assembly for various applications, providing instructors with a toolkit for teaching an adaptable and emergent cloning technology while advancing their research projects.

An adaptable Gibson Assembly molecular cloning module was employed in a course-based undergraduate research experience (CURE) format for molecular biology laboratory course students. Assessment of student learning outcomes showed improved understanding and confidence in molecular cloning after completion of the CURE, and novel plasmids were cloned for natural product biosynthesis research.&#160;&#160;

The continuous advancement of molecular biology techniques requires that molecular biology curricula are&#160;regularly refined to effectively prepare ...

Biochemistry

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications.
We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm.
Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents.
CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

We present a protocol and associated programming code as well as metadata samples to support a cloud-based automated identification of phrases-category association representing unique concepts in user selected knowledge domain in biomedical literature. The phrase-category association quantified by this protocol can facilitate in depth analysis in the selected knowledge domain.

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining ...

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Increasing the diversity of students choosing careers in science, technology, engineering, and mathematics (STEM) fields is an area of intense focus across the USA, especially in kindergarten through 12th grade (K-12)-focused pipeline programs in medical schools. A diverse STEM workforce contributes to better problem-solving and equity in health care. Two of the many major barriers for rural students are the lack of sufficient STEM role models and limited access to technology in the classroom. Medical schools often serve as an important resource for students in the local community who can easily gain access to STEM professionals and modern technology through on-campus, sponsored events and STEM outreach to the local classrooms. However, underrepresented minority (URM) students often live in socioeconomically distressed parts of rural states such as Arkansas, where access to STEM role models and technology is limited. Virtual learning in the COVID-19 era has proven that the imaging technology resources of a medical school can be harnessed to reach a wider audience, especially students living in rural areas far from the medical school campus.

This article presents an overview of how synchronous web-based virtual outreach can be used to expose 6th-12th grade students to advanced imaging technologies such as ultrasound, computerized tomography, and electroencephalography. The paper discusses the methods and equipment needed to livestream integrated educational sessions for effective student engagement in STEM.

Increasing the diversity of students choosing careers in science, technology, engineering, and mathematics (STEM) fields is an area of intense focus ...

Training Novices in POCUS for Vascular Cannulation Using Simulation Models

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

The use of point-of-care ultrasound (POCUS) has shown to be a beneficial non-invasive vascular access assessment method by clinicians, which can provide critical elements of visual and measurable information that proves to be useful in the context of vascular access cannulation, in combination with the practical skill of the clinician performing the cannulation. However, the use of POCUS in this context is to practically train and enable individuals who are novices in using this technique to become proficient in performing this task subsequently on patients in a careful and successful way. The simulation of these vascular conditions may be useful to help healthcare professionals learn, understand, apply, and establish such practical skills for vascular cannulation safely to achieve the desired outcomes. This project intended, through the attendance of a half-day workshop, to establish skills to use POCUS in connection with simulation models and perform specific tasks to enable clinicians to use this method in their clinical practice for vascular access cannulation in patients. A mixed-methods longitudinal study design was used to evaluate the effect of a point-of-care ultrasound workshop for peripheral intravenous cannula insertion, including specific tasks for the participants to be performed on simulation models. A total of 81 individuals participated in 11 half-day workshops through 2021 and 2022. Offering a workshop that uses simulation models in combination with various POCUS devices is useful in establishing this newly learned skill in clinicians, such as measurements of depth, caliper, and direction of a vein with POCUS prior to cannulation providing essential anatomical facts to the operator, which increases the likelihood of first-time success in cannulation.

Author Spotlight: Evaluating Clinicians' Adoption of Ultrasound-Guided Vascular Cannulation Through Simulation Training

The present protocol describes an ideal solution to train novices in the use of point-of-care ultrasound devices for the practical clinical skill of visually assessing distinct individual anatomical vascular conditions prior to and during an intended venous vascular cannulation using point-of-care ultrasound in a patient.

The use of point-of-care ultrasound (POCUS) has shown to be a beneficial non-invasive vascular access assessment method by clinicians, which can provide ...

POC Method and Integrated Decision Support Tool for Estimating Anemia

A Point-of-Care Method with Integrated Decision Support Tool to Estimate Anemia at Population Level

Robust point-of-care methods are required to estimate anemia at the population level. The accurate methods are lab-based and cannot be used at the point of care. To address this caveat, a novel method based on pooled capillary blood and a portable autoanalyzer was developed for the estimation of Hb. Additionally, custom software was developed for near-real-time integration of the Hb values from the auto analyzer to the server. Moreover, a decision support tool that can immediately categorize the participants into different stages of anemia was developed. The decision support tool was designed based on the World Health Organization (WHO) cut-off for anemia at the population level and was available for all age and gender groups. This simple and user-friendly tool could&#160;easily be used by front-line health workers who have limited technical skills. Overall, the method developed could&#160;be used at the point of care and is accurate. This high-throughput method could&#160;be used for screening anemia at the population level for all age and gender groups.

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

An accurate hemoglobin estimation method is lacking at the point of care and may hinder population-based programs for treating anemia. Therefore, we developed a point-of-care method based on pooled capillary blood and an auto-analyzer integrated into a custom software application to categorize the hemoglobin values into different grades of anemia.

Robust point-of-care methods are required to estimate anemia at the population level. The accurate methods are lab-based and cannot be used at the point ...

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

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