With this 100% online university degree, you will acquire advanced competencies in Educational Research, using innovative analytical tools” 

Educational Research is the driving force behind pedagogical evolution, enabling the design of evidence-based strategies to optimize learning and teaching. Currently, the rise of digitalization and data analysis has transformed the way educational processes are evaluated, increasing the demand for professionals with advanced research skills. In fact, the Ministry of Education and Vocational Training has emphasized the importance of strengthening teaching research as a key avenue to improve the educational system. 

In this context, educators and education professionals face the challenge of enhancing their analytical and methodological skills to meet the sector's demands. For this reason, TECH has developed this Master's Degree in Educational Research, a rigorous and updated academic experience that delves into the most innovative trends in the research field. 

Throughout this academic journey, graduates will explore qualitative and quantitative methodologies applied to education, as well as advanced data analysis techniques and pedagogical evaluation. The most commonly used digital tools in Educational Research will also be addressed, allowing educators and professionals to design projects with a real impact on teaching. In this way, graduates will be prepared to lead studies in academic institutions, develop effective evaluation models, and contribute to educational innovation with an evidence-based approach. 

At the same time, this degree is offered with a 100% online methodology, allowing educators and professionals to balance their learning with their professional and personal responsibilities. All content is available 24/7, accessible from any device and downloadable for reference. Additionally, this university program includes the Relearning learning system, which guarantees effective assimilation of concepts through the strategic repetition of key knowledge. 

Develop advanced competencies in Educational Research, applying cutting-edge analytical models and guiding future researchers in the academic field” 

This Master's Degree in Educational Research contains the most complete and up-to-date educational program on the market. Its most notable features are: 

• The development of practical cases presented by experts in Educational Research 
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice 
• Practical exercises where the self-assessment process can be carried out to improve learning 
• Special emphasis on innovative methodologies in Educational Research 
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments 
• Content that is accessible from any fixed or portable device with an Internet connection 

You will reach your full potential in Educational Research with the help of multimedia resources such as interactive summaries, explanatory videos, and specialized readings” 

The faculty includes professionals from the field of Educational Research, who bring their work experience into the program, alongside recognized specialists from leading organizations and prestigious universities. 

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive learning experience designed to prepare for real-life situations. 

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional will be assisted by an innovative interactive video system created by renowned and experienced experts. 

You will efficiently explore educational analysis models, allowing you to foresee and address various current pedagogical challenges"

You will have access to a learning system based on repetition, with natural and progressive teaching throughout the curriculum, optimizing your understanding and application of key concepts"

This university program from TECH aims to provide a comprehensive and advanced understanding of Educational Research. Throughout the program, professionals will develop skills in designing qualitative and quantitative studies, mastering data collection and analysis techniques. They will also acquire competencies in the use of specialized computer tools, applied to pedagogical evaluation and the improvement of educational processes. In this way, they will optimize teaching methodologies and promote inclusive, equitable, and creative models in diverse educational environments.  

You will delve into research methodologies, using advanced computer tools for data analysis and interpretation in the educational field” 

 

Module 1. Fundamentals, Processes and Methods in Research 

1.1. Methodological Design of Educational Research 

1.1.1. Introduction
1.1.2. Approaches or Paradigms in Educational Research
1.1.3. Types of Research

1.1.3.1. Basic or Fundamental Research
1.1.3.2. Applied Research
1.1.3.3. Descriptive or Interpretative Research
1.1.3.4. Prospective Research
1.1.3.5. Exploratory Research 

1.1.4. The Process of Research: The Scientific Method 

1.2. Statistical Analysis of Data 

1.2.1. Introduction
1.2.2. What is data Analysis?
1.2.3. Types of Variables
1.2.4. Measuring Scales 

1.3. Univariate Descriptive Statistics (I): Frequency Distribution and Frequency Polygon 

1.3.1. Introduction
1.3.2. Frequency Distribution
1.3.3. Frequency Polygons or Histograms
1.3.4. SPSS: Frequencies 

1.4. Univariate Descriptive Statistics (II): Position Indexes and Dispersion Indicators 

1.4.1. Introduction
1.4.2. Variables and Types
1.4.3. Position or Central Tendency Indices and their Properties

1.4.3.1. Arithmetic Mean
1.4.3.2. Median
1.4.3.3. Fashion

1.4.4. Dispersion or Variability Indexes 

1.4.4.1. Variance
1.4.4.2. Standard Deviation
1.4.4.3. Coefficient of Variation
1.4.4.4. Semiquartile Amplitude
1.4.4.5. Total Amplitude 

1.5. Univariate Descriptive Statistics (III): Scores and Shape of Distribution Index 

1.5.1. Introduction
1.5.2. Types of Scores

1.5.2.1. Differential Score
1.5.2.2. Typical Score
1.5.2.3. Centile Score

1.5.3. Distribution Shape Index 

1.5.3.1. Asymmetry Index (AS)
1.5.3.2. Kurtosis or Kurtosis Index (Cv) 

1.6. Exploratory Data Analysis (EDA)

1.6.1. Introduction
1.6.2. Definition of Exploratory Data Analysis
1.6.3. Stages of Exploratory Data Analysis
1.6.4. SPSS: Exploratory Data Analysis 

1.7. Linear Correlation Between Two Variables (X and Y)

1.7.1. Introduction
1.7.2. Concept of Correlation
1.7.3. Types and Correlation Coefficients
1.7.4. Pearson’s Correlation Coefficient (rxy)
1.7.5. Properties of Pearson’s Correlation
1.7.6. SPSS: Correlation Analysis 

1.8. Introduction to Regression Analysis 

1.8.1. Introduction
1.8.2. General Concepts: The Regression Equation of Y on X
1.8.3. Model Goodness-of-fitIindex
1.8.4. SPSS: Linear Regression Analysis 

1.9. Introduction to Inferential Statistics (I) 

1.9.1. Introduction
1.9.2. Probability: General Concept
1.9.3. Contingency Tables for Independent Events
1.9.4. Theoretical Probability Models with Continuous Variables

1.9.4.1. Normal Distribution
1.9.4.2. Student’s T Distribution 

1.10. Introduction to Inferential Statistics (II) 

1.10.1. Introduction
1.10.2. Theoretical Probability Models With Continuous Variables
1.10.3. Sample Distribution
1.10.4. The Logic of Hypothesis Testing
1.10.5. Type I and II Errors

Module 2. Experimental Research: Design as a Model

2.1. Experimental Method 

2.1.1. Introduction
2.1.2. Approaches or Paradigms from Educational Research
2.1.3. Concept of Experimental Research
2.1.4. Types of Research
2.1.5. Research Approach
2.1.6. Quality of Research: Kerlinger Principle (Max-Min-Con)
2.1.7. Experimental Validity of an Investigation

2.2. Experimental Design in Research 

2.2.1. Introduction
2.2.2. Types of Experimental Designs: Pre-experimental, Experimental, and Quasi-experimental
2.2.3. Experimental Control 

2.2.3.1. Controlling Variables
2.2.3.2. Control Techniques
2.2.3.3. Experimental Designs: Between-Group and within-Subject Design
2.2.3.4. Analysis of Data: Statistical Techniques 

2.3. Experimental Design with Different Groups of Subjects 

2.3.1. Introduction
2.3.2. Approaches or Paradigms from Educational Research
2.3.3. Concept of Experimental Research
2.3.4. Types of Research
2.3.5. Research Approach
2.3.6. Quality of a Research, Kerlinger’s Principle (Max-Min-Con)
2.3.7. The Validity of an Investigation 

2.4. Experimental Design with the Same Subjects

2.4.1. Introduction
2.4.2. Student’s T-test with the Same Subjects
2.4.3. Non-parametric Contrasts for Two Related Samples Wilcoxon Test
2.4.4. Non-parametric Contrasts for Two Related Samples: Friedman Test 

2.5. One-factor Completely Randomized Experimental Design 

2.5.1. Introduction
2.5.2. The general Linear Model
2.5.3. Anova Models
2.5.4. One-factor, Fixed-effects, Completely Randomized Anova (A-FE-CR) 

2.5.4.1. The Model
2.5.4.2. The Assumptions
2.5.4.3. The Contrast Statistic 

2.5.5. Measures of Effect Size
2.5.6. Multiple Comparisons Between Measurements 

2.5.6.1. What are Multiple Comparisons?
2.5.6.2. A Priori Planned Comparisons
2.5.6.3. Ex-post Planned Comparisons 

2.6. One-factor Experimental Design with Repeated Measures 

2.6.1. Introduction
2.6.2. One-factor, Fixed-effects, Completely Randomized Anova (A-FE-CR)
2.6.3. Measures of Effect Size
2.6.4. Multiple Comparisons 

2.6.4.1. Planned Orthogonal Comparisons: Planned F Tests

2.7. Completely Randomized Two-Factor Experimental Design 

2.7.1. Introduction
2.7.2. Two-factor, Fixed-effect, Completely Randomized Anova (AB-FE-CA)
2.7.3. Measures of Effect Size
2.7.4. Multiple Comparisons 

2.8. One-factor Experimental Design with Repeated Measures 

2.8.1. Introduction
2.8.2. Two-Factor ANOVA with Fixed Effects, with Repeated Measures in Both Factors
2.8.3. Multiple Comparisons
2.8.4. Two-factor, Fixed-effects, Anova with Repeated Measures on a Single Factor
2.8.5. Multiple Comparisons 

2.9. Block Experimental Design

2.9.1. Introduction
2.9.2. Characteristics of Block Designs
2.9.3. Additional Variables to the Factor: Blocking Factor
2.9.4. One-factor Blocking Design: Completely Randomized Blocking
2.9.5. Two-factor Blocking Design: Latin Square Blocking

2.10. Experimental Design with Covariate Variables

2.10.1. Introduction
2.10.2. ANCOVA design

2.10.2.1. Covariate Variables to Reduce the Error Term
2.10.2.2. Covariate Variables to Control Extraneous Variables

2.10.3. Why Include a Covariate Variable in the Design?
2.10.4. Blocking and ANCOVA

2.11. Single Case Experimental Design (N=1)

2.11.1. Introduction
2.11.2. Basic Structure of Single-case Designs

2.11.2.1. Elaboration of Multiple Items
2.11.2.2. Difficulty Index, Discrimination Index, Validity Index
2.11.2.3. Analysis of Distractor Items

2.11.3. Treatment Study in Single Case Design

2.11.3.1. Visual Data Analysis 

2.11.4. Basic Model: A-B
2.11.5. A-B-A Design
2.11.6. Criteria Change Design
2.11.7. Multiple Baseline Design

Module 3. Techniques and Instruments for Data Collection in Qualitative Research

3.1. Introduction

3.1.1. Qualitative Research Methodology
3.1.2. Qualitative Research Techniques
3.1.3. Phases of Qualitative Research

3.2. Observation

3.2.1. Introduction
3.2.2. Observation Categories
3.2.3. Types of Observation: Ethnographic, Participant and Non-participant
3.2.4. What, How and When to Observe?
3.2.5. Ethical Considerations of Observation
3.2.6. Content Analysis

3.3. Interview Techniques 

3.3.1. Introduction
3.3.2. Interview Concept
3.3.3. Interview Characteristics
3.3.4. The Purpose of the Interview
3.3.5. Types of Interviews
3.3.6. Advantages and Disadvantages of the Interview

3.4. Discussion Group and Focus Group Techniques 

3.4.1. Introduction
3.4.2. Discussion Groups
3.4.3. Objectives that Can Be Considered: Advantages and Disadvantages
3.4.4. Issues for Discussion 

3.5. SWOT and DELPHI Technique 

3.5.1. Introduction
3.5.2. Characteristics of Both Techniques
3.5.3. SWOT Technique
3.5.4. The Delphi Technique.

3.5.4.1. Preliminary Tasks Before Starting a DELPHI 

3.6. Life History Method 

3.6.1. Introduction
3.6.2. Life History
3.6.3. Method Characteristics
3.6.4. Types
3.6.5. Phases 

3.7. The Field Diary Method 

3.7.1. Introduction
3.7.2. Concept of Field Diary
3.7.3. Field Diary Characteristics
3.7.4. Structure of the Field Diary 

3.8. Discourse and Image Analysis Technique 

3.8.1. Introduction
3.8.2. Characteristics
3.8.3. Discourse Analysis Concept
3.8.4. Discourse Analysis Types
3.8.5. Levels of Discourse
3.8.6. Image Analysis 

3.9. The Case Study Method 

3.9.1. Introduction
3.9.2. Concept of Case Studies
3.9.3. Types of Cases Study
3.9.4. Design of the Cases Study 

3.10. Classification and Analysis of Qualitative Data 

3.10.1. Introduction
3.10.2. Categorization of Data
3.10.3. Data Coding
3.10.4. Theorizing Data
3.10.5. Data Triangulation
3.10.6. Exposure of Data
3.10.7. Writing Analytical Reflections. Memoing

Module 4. Computational Resources for Educational Research

4.1. Documentary Resources in Educational Research 

4.1.1. Introduction
4.1.2. Introduction of Documentary Resources in Educational Research
4.1.3. Dissemination and Communication of Scientific-Academic Information
4.1.4. Academic Scientific Language
4.1.5. Access to Information: Bibliographic Databases 

4.2. Information Search and Retrieval 

4.2.1. Introduction
4.2.2. Search for Information
4.2.3. Information Search Strategies: Interfaces
4.2.4. Search for Electronic Journals
4.2.5. Bibliographic Databases 

4.3. Access to Information Sources 

4.3.1. Introduction
4.3.2. Databases
4.3.3. Electronic Magazines
4.3.4. Institutional Repositories
4.3.5. Scientific Social Networks
4.3.6. Information Managers

4.4. Thesauri 

4.4.1. Introduction
4.4.2. Concept of Thesaurus
4.4.3. Characteristics of Thesaurus
4.4.4. Terminology of Thesaurus 

4.5. Thesauri: Use of the Database 

4.5.1. Introduction
4.5.2. Thesaurus Nomenclature
4.5.3. Thesaurus Hierarchy
4.5.4. Database 

4.6. Information Evaluation Criteria 

4.6.1. Introduction
4.6.2. Criteria for Evaluating Bibliographic Sources
4.6.3. Bibliometric Indicators
4.6.4. Book Evaluation and Publisher Ranking

4.7. Communication of Information 

4.7.1. Introduction
4.7.2. Academic Scientific Language
4.7.3. Communication of Information
4.7.4. The Scientific Publication Process 

4.8. SPSS (I)-Statistical Computing Tool Quantitative Data 

4.8.1. Introduction
4.8.2. Introduction to SPSS
4.8.3. Structure of SPSS
4.8.4. How to Handle Data Files? 

4.9. SPSS (II)- Descriptive Analysis of Variables 

4.9.1. Introduction
4.9.2. Menu Bar and SPSS tools
4.9.3. Create New Files
4.9.4. How to Define a Variable? 

4.10. Computer Resources, Qualitative Data 

4.10.1. Introduction
4.10.2. Programs and Resources for Qualitative Data Collection
4.10.3. Computer Resources for Analyzing Qualitative Data
4.10.4. Other Programs for Information Analysis

Module 5. Data Collection Techniques and Instruments and Measurement

5.1. Measurement in Research 

5.1.1. Introduction
5.1.2. What do we Want to Measure?
5.1.3. Subject Measurement Process
5.1.4. Psychometry 

5.2. Collection of Information using Quantitative Techniques: Observation and Surveys

5.2.1. Introduction
5.2.2. Observation 

5.2.2.1. Theoretical Framework and Categories of Observation 

5.2.3. The Survey 

5.2.3.1. Material for Conducting a Survey
5.2.3.2. Survey Research Design 

5.3. Collection of Information with Quantitative Techniques: The Tests 

5.3.1. Introduction
5.3.2. Test Concept
5.3.3. Item Generation Process
5.3.4. Testing by Area: Performance; Intelligence and Aptitude; Personality, Attitudes and Interests

5.4. Collection of Information with Quantitative Techniques: Scaling Methods

5.4.1. Introduction
5.4.2. Concept of Attitude Scales
5.4.3. Thurstone Method 

5.4.3.1. Method of Paired Comparisons

5.4.4. Likert Scale
5.4.5. Guttman Scale 

5.5. Test Construction Process 

5.5.1. Introduction
5.5.2. Item Scaling Process 

5.5.2.1. Item Generation Process
5.5.2.2. Information Gathering Process
5.5.2.3. Scaling Process in the Strict Sense 

5.5.3. Scale Evaluation Process 

5.5.3.1. Item Analysis
5.5.3.2. Scale Dimension
5.5.3.3. Scale Reliability
5.5.3.4. Scale Validity 

5.5.4. Subjects’ Scores on the Scale 

5.6. Analysis of Test Items 

5.6.1. Introduction
5.6.2. Classical Test Theory (Spearman, 1904)
5.6.3. Test Reliability
5.6.4. The Concept of Validity
5.6.5. Evidence of Validity 

5.7. Reliability of the Instrument

5.7.1. Introduction
5.7.2. Definition of Reliability
5.7.3. Reliability by Test-Retest or Repeatability Method
5.7.4. Reliability by the Alternate or Parallel Shape Method
5.7.5. Reliability Through Internal Consistency Coefficients 

5.7.5.1. Coeficiente de Kuder-Richardson
5.7.5.2. Cronbach’s Alpha Coefficient 

5.8. Validity of the Instrument 

5.8.1. Introduction
5.8.2. Definition of Validity
5.8.3. Validity of the Instruments 

5.8.3.1. Immediate Validity
5.8.3.2. Content Validity
5.8.3.3. Construct Validity
5.8.3.4. Contrast Validity 

5.8.4. Validity Strategies 

5.9. Item Analysis 

5.9.1. Introduction
5.9.2. Item Analysis
5.9.3. Difficulty and Validity Indexes
5.9.4. Correction of Random Effects 

5.10. Interpretation of Test Scores 

5.10.1. Introduction
5.10.2. Interpretation of Scores
5.10.3. Normative Test Scales
5.10.4. Typical Derived Baremos
5.10.5. Interpretations Referring to the Criterion

Module 6. Item Response Theory (IRT) 

6.1. Item Response Theory (IRT) 

6.1.1. Introduction
6.1.2. Measurement Models
6.1.3. Fundamental Concepts of IRT
6.1.4. Basic Postulates of IRT 

6.2. Generalizability Theory (GT)

6.2.1. Introduction
6.2.2. Generalizability Theory (GT)
6.2.3. Facets of Generalizability Theory
6.2.4. Interpretation of Results in a Study 

6.3. Characteristics of IRT (I) 

6.3.1. Introduction
6.3.2. Historical Introduction of TRI
6.3.3. IRT Assumptions
6.3.4. IRT models

6.4. Characteristics of IRT (II) 

6.4.1. Introduction
6.4.2. TRI Results

6.4.2.1. Parameters
6.4.2.2. Item Characteristic Curve
6.4.2.3. True Score
6.4.2.4. Test Characteristic Curve
6.4.2.5. Level of Information

6.4.3. Response Models: the Item Characteristic Curve
6.4.4. Question Selection Methods 

6.5. Response Models for Dichotomous Items: the Rasch Contribution

6.5.1. Introduction
6.5.2. The Rasch Model
6.5.3. Characteristics of the Rasch Model
6.5.4. Example (Rasch Model)

6.6. Response Models for Dichotomous Items: the Rasch Contribution 

6.6.1. Introduction
6.6.2. Birnbaum’s Logistic Model (1968)
6.6.3. Model Parameters 

6.6.3.1. 2-parameter Logistic Model
6.6.3.2. 3-parameter Logistic Model
6.6.3.3. 4-parameter Logistic Model 

6.7. Response Models for Polytomous Items: Nominal Item Models (Block, 1972) 

6.7.1. Introduction
6.7.2. Polytomous Items
6.7.3. Nominal Response Models (Block, 1972)
6.7.4. Political Item Parameters 

6.8. Response Models for Polytomous Items: Ordinal Item Models 

6.8.1. Introduction
6.8.2. Ordinal Item Models
6.8.3. Ordinal Cumulative Model 

6.8.3.1. Samejima’s Graded Response Model (GRM) (1969)
6.8.3.2. Modified Graded Response Model (M-GRM) of Muraki (1990) 

6.8.4. Continuous Ordinal Models 

6.8.4.1. Sequential Model (Tutz, 1990) 

6.8.5. Adjacent Ordinal Models 

6.8.5.1. Partial Credit Model (Masters, 1982) 

6.9. Response Model for Polytomous Items: Samejima’s Graded Response Model (1969) 

6.9.1. Introduction
6.9.2. Normal Graded Response Model
6.9.3. Graded Response Logistic Model
6.9.4. Example (Graded Response Model)

6.10. Differential Item Functioning (DIF)

6.10.1. Introduction
6.10.2. Concept of Differential Item Functioning (DIF)
6.10.3. Types of DIF
6.10.4. Methods for Detecting DIF
6.10.5. Purification Methods

Module 7. Multivariate Analysis

7.1. Multivariate Analysis

7.1.1. Introduction
7.1.2. What is Multivariate Analysis?
7.1.3. The objectives of Multivariate Analysis
7.1.4. Classification of Multivariate Techniques 

7.2. Multiple Linear Regression 

7.2.1. Introduction
7.2.2. Concept of Multiple Linear Regression
7.2.3. Conditions for Multiple Linear Regression
7.2.4. Predictors to Generate the Best Model 

7.3. Binary Logistic Regression 

7.3.1. Introduction
7.3.2. Binary Logistic Regression Concept
7.3.3. Model adjustment 

7.3.3.1. Model fitting in R 

7.3.4. Stages of the R
7.3.5. Example (Binary Logistic Regression)

7.4. Nominal and Ordinal Logistic Regression 

7.4.1. Introduction
7.4.2. General review of Nominal Logistic Regression
7.4.3. Example (Nominal Logistic Regression)
7.4.4. General review of Ordinal Logistic Regression
7.4.5. Example (Ordinal Logistic Regression) 

7.5. Poisson Regression

7.5.1. Introduction
7.5.2. Poisson Concept
7.5.3. Distribution Functions
7.5.4. Poisson Regression with Counts

7.6. Log-Linear Models 

7.6.1. Introduction
7.6.2. Log-Linear Models for Contingency Tables
7.6.3. Log-Linear Models for Contingency Tables
7.6.4.  Example (Log-Linear Models for Contingency Tables) 

7.7. Discriminant Analysis 

7.7.1. Introduction
7.7.2. Concept of Discriminant Analysis
7.7.3. Classification with Two Groups 

7.7.3.1. Fisher Discriminant Function 

7.7.4. Example (Discriminant Analysis) 

7.8. Cluster Analysis 

7.8.1. Introduction
7.8.2. Concept of K-means Clusters
7.8.3. Hierarchical Cluster Analysis Concept
7.8.4. Example (Hierarchical Cluster Analysis) 

7.9. Multidimensional scaling 

7.9.1. Introduction
7.9.2. Multidimensional Scaling: Basic Concepts
7.9.3. The Similarity Matrix
7.9.4. Classification of Scaling Techniques

7.10. Factor Analysis 

7.10.1. Introduction
7.10.2. When is Factor Analysis Used?
7.10.3. Factor Analysis Methodology
7.10.4. Applications of Factor Analysis

Module 8. Thesis and Scientific Research Project Supervision, University Student Guidance

8.1. Motivating University Students to Get Involved in Research

8.1.1. Introduction to Investigative Practice
8.1.2. Gnoseology or Theory of Knowledge
8.1.3. Scientific Research and its Foundations
8.1.4. Research-Oriented Motivation 

8.2. Basic Student Training for Research Activity 

8.2.1. Initiation in Research Methods and Techniques
8.2.2. Elaboration of Quotes and Bibliographic References
8.2.3. The Use of New Technologies in Information Searching and Management
8.2.4. The research report: structure, characteristics and preparation standards 

8.3. Requirements for the Management of Research Projects 

8.3.1. Initial Guidance for Research Practice
8.3.2. Responsibilities in the Supervision of Theses and Research Projects
8.3.3. Introduction to Scientific Literature 

8.4. The Approach to the Topic and the Study of the Theoretical Framework 

8.4.1. The Research Topic
8.4.2. Objectives of the Research
8.4.3. Document Sources and Research Techniques
8.4.4. Structure and Boundaries of the Theoretical Framework 

8.5. Research Designs and the Hypothesis System 

8.5.1. Types of Studies in Research
8.5.2. Research Designs
8.5.3. Hypothesis: Types and Characteristics
8.5.4. Variables in Research

8.6. Research Methods, Techniques and Instruments 

8.6.1. Population and Sample
8.6.2. Sampling
8.6.3. Methods, Techniques and Instruments 

8.7. Planning and Supervision of Student Activity 

8.7.1. Research Plan Development
8.7.2. Research Activity Document
8.7.3. Schedule of Activities
8.7.4. Tracking and Monitoring of Students 

8.8. Supervision of Scientific Research Projects 

8.8.1. Promoting Research Activity
8.8.2. Encouragement and Creation of Opportunities for Enrichment
8.8.3. Resources and Presentation Techniques 

8.9. The Management of Master’s Theses and Doctoral Dissertations

8.9.1. Supervision of Master’s Theses and Doctoral Dissertations as a Pedagogical Practice
8.9.2. Mentoring and Career Planning
8.9.3. Characteristics and Structures of Master’s Theses
8.9.4. Characteristics and Structure of Doctoral Dissertations 

8.10. Commitment to the Dissemination of Research Results: The True Impact of Scientific Research 

8.10.1. Instrumentalization of Research Work
8.10.2. Toward a Meaningful Impact of Research Activity
8.10.3. Byproducts of Research Projects
8.10.4. Dissemination and Communication of Knowledge

Module 9. Innovation, Diversity and Equity in Education

9.1. What Do We Mean by Educational Innovation?

9.1.1. Definition
9.1.2. Why is Educational Innovation Important?
9.1.3. How Can We Be Innovative?
9.1.4. Should We Be Innovative?

9.2. Diversity, Equity and Equal Opportunity 

9.2.1. Definition of Concepts
9.2.2. Three Essential Elements in Education

9.3. Innovation and Educational Improvement 

9.3.1. Innovation Process
9.3.2. Efficiency and Educational Improvement 

9.4. Innovation for Achieving Equality in Education

9.4.1. How to Explain Equality
9.4.2. Equality in Education: A Persistent Problem
9.4.3. Factors for Achieving Equality in the Classroom: Examples in the Classroom

9.5. Non-Sexist Teaching and Language 

9.5.1. What is Non-Sexist Language?
9.5.2. What is Sexism in Language?
9.5.3. What is Inclusive Language?
9.5.4. Examples of Sexist and Non-Sexist Language in Education 

9.6. Factors that Favor and Hinder Innovation

9.6.1. Factors that Favor Innovation
9.6.2. Factors that Hinder Innovation

9.7. Characteristics of Innovative Schools 

9.7.1. What is an Innovative School?
9.7.2. Innovative Schools, a Different Education
9.7.3. Elements of an Innovative School
9.7.4. The Keys to an Innovative Classroom 

9.8. Process of Educational Innovation 

9.8.1. The 21st Century School 

9.9. Resources and Innovation Teaching Programs 

9.9.1. Distinct Innovation Programs Which Can Be Used in the Classroom
9.9.2. Teaching Resources for an Innovative Classroom 

9.10. Emerging Fields in the Teaching 

9.10.1. Emerging Pedagogies
9.10.2. Emerging Needs of Students
9.10.3. ICT as an Emerging Resource in Teaching
9.10.4. Different ICT Tools to Use in the Classroom

Module 10. Talent, Vocation, and Creativity

10.1. Talent and its Educational Importance 

10.1.1. Talent
10.1.2. Components
10.1.3. Talent is Diverse
10.1.4. Measuring and Discovering Talent
10.1.5. Gallup Test
10.1.6. GARP Test
10.1.7. CareerScope
10.1.8. MBTI
10.1.9. Success DNA 

10.2. Talent and Key Competencies 

10.2.1. Key Competencies Paradigm
10.2.2. Key Competencies
10.2.3. The Role of the Intelligences
10.2.4. Knowledge: Uses and Abuses in Education
10.2.5. The importance of Skills
10.2.6. The Differentiating Factor of Attitude
10.2.7. Relationship between Talent and Key Competencies

10.3. Talent Development 

10.3.1. Learning Modalities. Richard Felder
10.3.2. The Element
10.3.3. Talent Development Procedures
10.3.4. Mentor Dynamics
10.3.5. Talent and Educational Approach 

10.4. Motivation Mechanisms 

10.4.1. Needs, Desires and Motivations
10.4.2. Decision Making
10.4.3. Executive Capabilities
10.4.4. Procrastination
10.4.5. Duty, Love and Pleasure in Education
10.4.6. Emotional Habits for Motivation
10.4.7. Motivational Beliefs
10.4.8. Values for Motivation 

10.5. Vocation, Meaning and Purpose

10.5.1. The Importance of Vocation
10.5.2. Meaning and Purpose
10.5.3. Vision, Mission, Commitment
10.5.4. Exploring Vocation
10.5.5. Teaching Vocation
10.5.6. Educating for Vocation 

10.6. Towards a Definition of Creativity 

10.6.1. Creativity
10.6.2. Brain Functioning and Creativity
10.6.3. Intelligences, Talents and Creativity
10.6.4. Emotions and Creativity
10.6.5. Beliefs and Creativity
10.6.6. Divergent Thinking
10.6.7. Convergent Thinking
10.6.8. The Creative Process and Its Phases
10.6.9. Disney Dynamics 

10.7. Why Creativity? 

10.7.1. Arguments in Favor of Creativity Today
10.7.2. Personal Creativity for Life
10.7.3. Creativity in Art
10.7.4. Creativity for Problem Solving
10.7.5. Creativity for Professional Development
10.7.6. Creativity in the Coaching Process 

10.8. Creativity Development 

10.8.1. Conditions for Creativity
10.8.2. Artistic Disciplines as Precursors of Creativity
10.8.3. The Art Therapy Approach
10.8.4. Creativity Applied to Challenges and Problem Solving
10.8.5. Relational Thinking  
10.8.6. Edward de Bono’s Hats

10.9. Creativity as a Value in Education 

10.9.1. The Need to Encourage Creativity in Education  
10.9.2. Active Methodologies and Novelty  
10.9.3. Educational Models that Value Creativity  
10.9.4. Means, Times and Spaces to Apply Creativity in the Classroom  
10.9.5. Disruptive Education  
10.9.6. Visual Thinking  
10.9.7. Design Thinking 

10.10. Creative Techniques 

10.10.1. Relational Thinking Techniques  
10.10.2. Techniques for Generating Ideas  
10.10.3. Techniques for Evaluating Ideas  
10.10.4. Exercises of Ingenuity  
10.10.5. Artistic Disciplines for Creative Development  
10.10.6. RCS Method  
10.10.7. Other Techniques and Methods 

You will be able to identify students’ strengths and areas for improvement, providing them with the necessary support to reach their full potential”