MAART5100 Emotions and Relations in Art Therapy Course description

Different emotional and relational problems effect on mental health and are often at the core of art therapy. This course focuses on clinical art therapy practice with clients who have different mental challenges or diagnoses. Art therapy takes place in triangular relationship and interaction between client or group, art and therapist. The working alliance consists of the client’s emotional bond to the therapist and art as well as the negotiation of art therapeutic tasks and goals. This course focuses especially on the emotional and relational aspects that effect on the quality of working alliance, the group dynamics and outcome of art therapy. Students will develop self-reflective skills and learn to reflect emotional and relational aspects of the therapeutic bond. They will learn to recognize and repair ruptures within the therapeutic relationship.

The students’ own art therapy practice and its reflection from the emotional and relational viewpoint is at the core of learning during this course. It includes supervision of the student’s art therapy practice training. Students will learn to use different tools for measuring the quality of working alliance in art therapy. They will learn principles of systematic reporting and presenting of practice cases. The course includes supervision of students’ art therapy practice.

The students will have an assignment at the end of each module (4 in total) to consolidate the material learned in that week. All assignments need to be submitted in order to have access to the exam and to pass the course.

Admission to the program

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence: 

Knowledge

The student:

• can use principles of case-study to describe and communicate practice experiences
• understands emotional and relational theories, phenomena and problems within the framework of art therapy

• knows different research- and art-based tools for systematic gathering and reflection of emotional and relational data in art therapy

 Skills

The student:  

• can systematically assess and analyse the quality of therapeutic alliance and adapt own approach according to the findings
• knows research-based principles of rupture resolution and can apply them in own practice

• can critically reflect emotional and relational practice experiences and find constructive solutions for challenging situations

Competence

The student:

• can receive and utilize professional supervision, evaluation and feedback to develop practice related emotional, relational and reflective skills
• can present and communicate art therapy practice experiences and processes in a self-reflective, systematic and coherent manner

• can flexibly, creatively and constructively participate in professional reflections and change patterns of action to improve the quality of work, including own practice

The course is combining campus seminars with distant learning. Teaching and learning methods include lectures, seminars and skills training. The course includes 60 hours practice.

Skills training takes place in workshops. In professional experiential workshops, the focus is on simulating art therapeutic interaction to enable learning and reflection from the therapist perspective. In personal experiential workshops, students will explore and reflect art making and their own creative process from the client’s perspective.

The course includes minimum 60 hours of art therapy practice. Depending of the context of the practice and client group, it can be conducted both as physical or distant meetings. The practice can include individual or group art therapy, and if the context is relevant, it can also take place at own workplace. For a more detailed description of the practical training, look at the separate plan for practice.

The following coursework must have been approved in order for a student to take the exam:

• participation in seminars, compulsory attendance of 80 %
• participation in the practice, compulsory attendance of 90 %
• individual self-evaluation of the practice on a given evaluation sheet
• individual practice case presentation for teacher and fellow students

• 4 individual artistic response images based on the practice experiences with traditional or digital tools

The course requirements can be conducted using English or Scandinavian language.

Individual oral exam, either at the campus or distantly, which consists of:

Poster presentation of the practice case 10 min and 15 min discussion

The exam answer can be given in English or a Scandinavian language.

Because most young researchers in life and health sciences do not have a solid quantitative background, they face difficulties when analyzing data independently. This difficulty represents a major drawback in research. Students waste time learning analytical methods by themselves that could be more quickly learned with proper instruction and support. Additionally, the lack of convention or standards in some fields is a source of confusion that slows the learning process. As a consequence, the quality of insights and research productivity suffer. This course provides a comprehensive introduction to data science and big data applied to neuroscience research.

Its content is designed to train the participants in state-of-the-art techniques in data analysis and machine learning. This will enable the students to interact independently with the data and draw insights from them. The modules are organized so the participants have the opportunity to learn how to handle the most common data types (e.g., EEG, calcium imaging). Special attention is given to field-tested data management protocols, as they are critical for a fast transition from data acquisition to knowledge generation.

This is a hands-on course where the students will learn from implementing the analysis themselves with close supervision. The course will focus on case studies using data from real experiments; advanced students may choose to use their own data. The students will develop understanding through constant presentation of their work and dialectical reflection over their choices, results, and interpretations.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

Upon successful completion of this course, the student knows:

• theoretical and practical aspects of data management and data processing for different data types (electrophysiology, imaging, and behavior), so they can independently engage with the state-of-the art literature.
• how statistics, dimensionality reduction, signal processing, calculus, and supervised and unsupervised learning may be applied to data analysis in neuroscience research.
• commonly encountered problems in data science and the most trusted strategies to solve them.
• basic data visualization techniques and how to employ them in exploratory data analysis and scientific communication.

Skills

Upon successful completion of this course, the student can:

• apply data analysis techniques to formulate a hypothesis, collect data, preprocess it, analyze it, and reach conclusions about the data.
• plan, design and implement data analysis pipelines for the most common types of data in neuroscience.
• identify and define a problem and craft a solution using data analytics.
• critically assess the results as well as justify and explain the methodological choice.
• identify new opportunities for organizational change including process improvements, cost reduction, or efficiency improvements.

General competence

Upon successful completion of this course the student can apply:

• data analysis principles to neuroscientific data in the context of its own research.
• methods and tools for data analysis and visualization.
• heuristics and strategies commonly used in the research field to solve data analysis problems.

The teaching methodology is oriented by Bloom's taxonomy of educational goals, namely, recollection, understanding, application, analysis, evaluation, and creativity. To promote recollection, understanding, and application, the course will consist of seminars taught by the teaching staff of OsloMet and other guests (experts in neuroscience or data science fields), coding workshops and problem solving oriented projects. Students will actively participate by implementing the full data processing pipeline from extracting the raw data to building visualizations. The pipeline and good habits will be consolidated through repetition in different modules, contexts, and data types, which is known to promote generalization of the knowledge. Organized in pairs, the students will constantly have the opportunity to recollect, explain the content to each other, and justify their work, as well as to provide feedback to their partners. With the intent to prepare the students to go beyond the methods taught in the course, once per module, the participants will read relevant papers in neuroscience, and discuss how to implement their analysis.