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Oct 19, 2016 - The new field of Positive Technology combines current advances and ..... The Online, Open and Flexible Higher Education Conference 2016 - ...

Prevention of student dropout in higher distance education: Positive Technology Marcela Paz González-Brignardello Universidad Nacional de Educación a Distancia (UNED), Spain [email protected]

Ángeles Sánchez-Elvira Paniagua Universidad Nacional de Educación a Distancia (UNED), Spain [email protected]

Abstract Student dropout prevention is one of the most important tasks in higher distance education. The care services and students support units have, at present, the possibility of make use of a wide variety of technological resources that could facilitate information and orientation to a higher number of students and these possibilities should be explored by institutions The new field of Positive Technology combines current advances and possibilities of ICT technologies with the approach of Positive Psychology, a discipline that promotes wellbeing and the development of the strengths of individuals, organizations and societies. In this paper we report on the development of a pilot program, based on the contributions of Positive Technology, whose main aim is to prevent and reduce students’ Academic Procrastination through an online and self-applied and self-guided intervention program. Research on Procrastination has been highly related to students’ dropout, in general, and has proved to be even more relevant in distance education (González-Brignardello & Sánchez-Elvira Paniagua, 2013). The program has been designed to change emotions, beliefs and self-images related to the conduct of Academic Procrastination, mainly with new students. The intervention is based mainly on visualization or mental imagery. Aspects of persistence and abandonment of the program, one of the key questions in online applications, are analysed.

Keywords: student dropout, self-guided online intervention, Academic Procrastination, emotions, mental imagery.


1. Introduction Student dropout presents a high prevalence, as shown by various internationally studies (e.g. Ulriksen, Madsen, & Holmegaard, 2010; Woodley & Simpson, 2014). In Spain, the estimation of prevalence is around 27% in higher education since the implementation of the EHEA (Ministerio de Educación, Cultura y Deporte, 2015). These data makes a priority that educational institutions implement mechanisms to reduce dropout rates facilitating the integration of the new students into the educational system, and encouraging their persistence in the institutions during the years of university education. In this way, the effectiveness of higher education, measured by success rates, will improve.

As expected, in the field of distance and virtual education, the prevalence of abandonment is even higher (Simpson, 2012, Sánchez-Elvira Paniagua 2016, Woodley & Simpson, 2014) and rates are around 50%, according these reports. With regards to the factors that influence this phenomenon, Lee & Choi (2011) extracted, from the revision of studies published during the years 1999 to 2009 (a period of great growth of distance education thanks to the advances of internet), a set of factors that influence student dropout in online courses. These factors were categorized as follows: (a) academic background, (b) Relevant experiences, (c) skills, (d) psychological attributes, (e) course design, (f) institutional support, (g) interactions, (h) work commitment, and (i) supportive environment. These nine categories were grouped into three main sections; (A) Student factors, (b) Course / Program factors, and (c) Environmental factors (p. 604). Of these major factors, the student factor includes the 55% of the total number of factors, the environmental factors includes the 25% and factors of course / program includes only the 20% (Lee & Choi, 2011). In other revision, Sánchez-Elvira Paniagua (2014) concludes that Institutional and Personal factors are the two sides of students’ dropout.

Numerous studies have shown that among the characteristics of those students that more frequently fail and drop-out, we find difficulties to persevere related to a broad set of personality traits, behaviours, cognitions and emotions (among which we can found self-efficacy, motivation, time management and the ability to cope with adversity), that could be considered in terms of inefficient self-regulated learning characteristics and process (Pintrich & de Groot, 1990; Zimmerman, 2002). In contrast, those students who complete their studies are usually characterized by autonomous and self-regulated learning, intrinsic motivation, engagement, resilience, persistence, etc. with their own project of study (Sánchez-Elvira Paniagua, Fernández & Amor, 2006; Sánchez-Elvira Paniagua & González Brignardello, 2014).


Among all these personal characteristics, there is a behaviour that has been reported as highly prevalent among students and which is related to underperforming and abandonment. We refer to the delay or postponement of learning tasks, or the so-called Academic Procrastination (Ferrari, Johnson, & McCown, 1995). This dilatory behaviour is considered a stable personality trait, i.e., it is the expression of a tendency to exhibit a typical response in a variety of situations. Academic Procrastination is related to poor time management, deficit in study skills (Solomon & Rothblum, 1984), deficit of self-regulation (Klassen, Krawchuk, & Rajani, 2008; Steel, 2007), as well as perfectionism (Burns, Dittman, Nguyen, & Mitchelson, 2000), self-handicapping (Beck, Koons & Milgrim, 2000), fear of failure or success (Solomon & Rothblum, 1984), among others.

Due to the large number of variables related to Academic Procrastination, numerous and diverse models of intervention can be found in the literature (Ozer, Demir, & Ferrari, 2013; Schouwenburg, 2004). However, the studies report mixed results of the effectiveness of different interventions to overcome Academic Procrastination.

An interesting and promising perspective comes from the area of Information and Communication Technologies (ICT), as numerous prevention and psychological intervention programs currently incorporate resources based on ICT. In 2002, Norcross, Hedges, and Prochaska predicted there would be therapeutic interventions based on technology in one decade; nowadays there is a clear trend of continuous development in this field.

The implementation of technology interventions is, thus, greater and more creative in this moment. On one hand, we have a complementary use of technical resources to optimize traditional psychotherapeutic approaches and maximize range of treatments to more people (Bunge, Lopez, Mandil, Gomar, & Borgialli, 2009); and on the other hand, we have specific interventions based on technological innovations such as Virtual Reality techniques (e.g. Botella, Baños, Villa, Perpiñá, & García-Palacios, 2000). Within this set of developments, we can also find the implementation of automated or semi-automated assessment systems using online test, and the development of online sessions via videoconference and cyber-therapies, as well (Eells, Barrett, Wright, & Thase, 2014).

Some of the advantages of these psychological interventions are accessibility, convenience, costeffectiveness, anonymity and privacy. Also, with online interventions it is possible to provide reinforcements and promote adherence to treatment, etc. (Eells et al., 2014). It should be noted that, once


these systems have been developed, the costs are low, both in maintenance and upgrade ones, in comparison to one to one interventions (Distéfano, O’Conor, Mongelo, & Lamas, 2015). In addition, the internet-based self-guided programs have lower cost and higher therapy efficiency.

With regard to the disadvantages against the online programs, we could point out that they lack of direct contact (although research data show even contradictory and inconclusive results about this); also, there might present some problems with security measures and safeguards of confidentiality; and, finally, the difficulty of handling a sudden critical situation, not uncommon in a therapy session.

But psychology has not only developed methods of intervention aimed at overcoming psychopathological processes or maladaptive ones. In the late 90s, we saw the birth of a new discipline that aims to study the optimal functioning of the human being (Seligman & Csikszentmihalyi, 2000). By means of scientific research, Positive Psychology, on one hand, tries to understand the characteristics of the personality that are related to the wellbeing and adaptation of people and the proper functioning of the institutions and societies and, on the other hand, it aims at developing programs that help to improve the lives of individuals. All this will result in the promotion of wellbeing, broadly defined, and specifically the prevention of psychopathological disorders (Seligman & Csikszentmihalyi, 2000). However, to understand how individuals’ wellbeing is generated and how it increases, in a wide sense, we need to understand the role of emotions, thoughts, values and behaviours and how they relate to live in an adaptive and satisfactory way.

In addition, and in line with the use that society makes of technology in all areas, Positive Technology appears as a new framework that integrates the principles of Positive Psychology with the advances and possibilities of technology.

Coming to the student’s wellbeing area of research, under a theoretical perspective it is assumed that students experience a lot of emotions related to academic life; however, studies report mostly about anxiety and its relationship to the study process and academic performance, with minimal research on other emotions (Pekrun, Goetz, Titz, & Perry, 2002). Nevertheless, this situation is remitting in last years, as new lines of research are showing how emotions also influence the process of self regulated learning (Pekrun, Goetz, Frenzel, Barchfeld, & Perry, 2011). Even more, Mega, Ronconi, & De Beni (2014) founded that students’ positive emotions positively affect their organization of academic study and summarization of study materials in a more personal way (p. 128).


Pekrun et al., 2002) proposed to use the term "academic emotions" to refer to the emotions of the area of learning, in line with other terms such as "academic motivation" or "academic self-efficacy." Academic emotions do not refer only to success and failure, but also to the learning or study process. Through five qualitative studies, these authors found different categories of discrete emotions appearing in several types of academic situations. The anxiety was mentioned more often, not only in relation to exams situations, but also to other academic situations, like studying at home or staying in class. Aside from anxiety, emotions that were most often reported were enjoyment of learning, hope, pride and relief, as well as anger, boredom, and shame (Pekrun et al., 2002, p. 93).

The psychological intervention called exposure is a specific technique that has been validated for anxiety disorders treatment, like phobia or posttraumatic stress. This technique has different modalities of presentation, for example, in real way (in vivo exposure) or exposure in imagination (mental exposure). In the mental exposure technique, the individuals focus on the fear scenes related to their main problem. The persons put into their minds the images, so this intervention is call “visualization” or “mental imagery” (e.g. Bullock, Newman-Taylor, & Stopa, 2016; Holmes, Geddes, Colom, & Goodwin, 2008).

In exposure interventions, patients are asked to recall the details of the feared event while focusing their attention on any occurring sensory feelings, thoughts, and emotions. In this way, exposure to such memories results in reduction of fear and avoidance. From a cognitive behavioural view, exposure objectives are habituation and extinction of phobic response to the feared stimulus. The presumed underlying mechanism is the loosening of the association between unconditioned and conditioned stimuli (e.g. Foa & Kozak, 1986; Foa, Dancu, Hembree, Jaycox, Meadows & Street, 1999).

From a psychoanalytic perspective, visualization imagination (mental imagery) is the reworking of memories, or rewriting the script (Arntz, Tiesema, & Kindt, 2007) providing, thus, of new information to memory system and storage, which will finally allow more adaptive responses to internal and external stimuli.

The main aim of this research was to implement a self-guided online intervention primarily based on mental visualization with the aim to change negative attitudes and emotions related to study that could be leading distance education students to procrastinate. The content of the visualization was related to bad memories related to learning episodes, or the study session itself. The participants were students who scored high in the Academic Procrastination scale.




2.1 Participants The participants were college freshmen in UNED distance higher education system and who were registered in the virtual community of induction of their Faculty. These students had been identified as risk students by giving high score (> P70) in the Short Academic Procrastination Scale. Participation was on a voluntary basis and one ECTS was offered. The entire procedure was conducted online. The participants signed an informed consent.

2.2 Self-reported Measures Academic Procrastination Scale – short version, (González-Brignardello & Sánchez-Elvira, n.d.). Experimental Instrument developed for research purposes It is a 6-items scale to which one responds with the degree of agreement on a five-point Likert scale. Positive and Negative Affect Schedule – PANAS (Watson, Clark & Tellegen, 1988), Spanish version (Sandín et al., 1999). Engagement Scale, (Salanova, Schaufeli, Llorens, Peiró & Grau, 2000). Adapted version: 7- items scale, to which one responds with the degree of agreement on a seven points Likert scale. Experimental Questionnaires Intra Session - experimental instrument (González-Brignardello, n.d.).

2.3 Procedure A quasi-experimental design was used, without control group and repeated measures pre-post intervention. 951 students responded to the online screening test. 347 of them were above the 70th percentile, of which 61 were finally enrolled in the program. The program was implemented 3 weeks before the exams periods. Those students that were enrolled in the program had access to a website inside UNED e-learning platform (aLF). In this site, they could find the intervention program and all self-reported measures, as well. Only 10 students ended up the program.

Intervention was developed as a sequential program consisting of 4 phases: -

Time available: students had to identify the amount of free time on a daily basis making use of a selfcalculation file. This activity was made only once because it consisted in delivery the file completed with student’s own information.



Beliefs about Procrastination and identification of personal style (multimedia presenter). This activity had lasted 5 minutes and, at the end, the student had to fill a form with conclusions about his/her procrastination style. This activity was made only once.


Visualization I (exposure to 1 own image while procrastinating) (15 minutes of duration). This exercise was made only once a day, but it was repeated before each three study sessions.


Visualization II (exposure to 2 own images: the first one while procrastinating and the second one to an image “totally different or opposite to the first one”) (15 minutes of duration). This exercise was made once, but if at the end of the session images continued appearing in a similar shape than at the beginning, then it was necessary to repeat the exercise.

The contents were developed in a multimedia format and interactive system. The access to each sequence of the program was progressively obtained through a secret password. The information for the next step was at the end of each exercise. The access to the next exercise was only possible with the password. The Intra-session measures were applied during the practice session through brief online forms inserted between the exercises.

Clear instructions for each visualization sessions were given before the starting point of each study session.



Only 10 students completed the program. Table 1 show abandonment occurred during the study.

Table 1: data of abandonment Nº students High Academic Procrastination Students enrolled

347 61

Activity Free time


Procrastination Style


1st session


2nd session


Ended the program



Pre – Post analyses Quantitative analyses The Wilcoxon signed rank test was used to compare pre and post intervention measures. -

The Academic Procrastination was significantly diminished between pre and post measures (Med1= 22,50 IQR1= 4,75; Med1=14,00 IQR2=10,25; Z= -2.103b; p= .035).


Students’ Engagement improved significantly (Med1= 25,60 IQR1= 11,00; Med2=34,00 IQR2=11,75; Z=2,670c; p=,008).


In the same way, Positive Affect increased significantly (Med1=36,00 IQR1=7,00; Med2=40,00 IQR2=12,50; Z=-2.383c; p=.017). In contrast, Negative Affect did not present any significant difference.

Qualitative analyses Analyses of emotional and cognitive changes. The qualitative analyses related to self-reports on students’ visualization shown changes in shape, colour and size in 9 of 10 students. The most important results indicated important changes on feelings and cognitions between the first visualization session and the final one. More detailed changes on emotions are described below. The intensity of emotions was measured in 0-10 scale. Student 1: anguish (8)  relief and satisfaction (8) “I haven´t patience for continuing”  “I have overcome” Student 2: fear (8)  happy (9) “I'm stupid and not worth for anything”  “I am strong and can handle anything” Student 3: anguish and shame (6)  optimism (7) “guilt”  “I´m studying without troubles and I´m happy” Student 4: suffocation (8)  pride and satisfaction (10) “guilt”  “achievement with perseverance and effort” Student 5: fatigue (5)  satisfaction (8) ….

 “triumph”

Student 6: disappointment (7)  satisfaction (8) “I do not try hard enough and always look for excuses”  “I´m able” Student 7: overwhelmed (5)  satisfaction (8) “I can not take all that I have to do and study”  “I can, I'm worth”


Student 8: nervousness (8)  happy and motivation (8) “I know not organize”  “I can, I can get it” Student 9: nervousness (9)  pride (9) “Again equal. You do it again”  “you're great” Student 10: sadness (3)  strength (8) “I'm useless”  “if you want you can do” We can see that in all students was produced a big change about the first sensation that made them self-image. The same occurred with first cognition.



Failure and dropout rates are high in university students, especially among distance education ones (Sánchez-Elvira Paniagua, 2014; Simpson, 2012). Although some institutional reasons can be underneath these high rates, there are also some personal characteristics to be considered (Lee & Choi, 2011; SánchezElvira Paniagua, 2014). A relevant personality trait in academic settings is students’ Procrastination, which has proved to deteriorate significantly students’ s performance and wellbeing (González-Brignardello & Sánchez-Elvira Paniagua, 2014). However, distance education students can be trained to prevent and reduce their procrastination and to develop self-regulated learning strategies.

In the present study, an innovative approach to support students in online environments was developed through the implementation of an online and web-based self-guided intervention oriented to change those negative cognitions, emotions and motivational states, that procrastinators experiment while they are studying. The online program was based on the use of mental imagery to change students’ previous negative visualizations about their experience while studying. Results showed that, after the program, students had developed more adaptive strategies, a significant increase of their engagement and positive affect, and a decrease of their self-referred academic procrastination.

In the present study, the online self-applied technique was derived from the Positive Psychology objectives (Seligman & Csikszentmihalyi, 2000) to whom the recent Positive Technology field is contributing (Riva, Baños, Botella, Wiederhold, Gaggioli, 2012); that is, ICT serving to enhance, in our case, students’ skills, learning strategies and wellbeing, and to promote the emergence of personal resources (such as positive emotions, empowering beliefs, positive thoughts, self-efficacy, etc.).


Taking into account the big challenges and difficulties of delivering guidance, orientation and support to large number of students in online environments, these new approaches could be easily adapted to a massive scale and a low cost without losing the capacity of producing significant and stable changes. Also, mobile technologies will allow us the implementation of even more personalized and adaptive mechanisms of intervention. In this sense, in UNED, this type of applied projects and research are been integrated in the development of the e-SPA, the online Applied Psychological Service of the Faculty of Psychology, contributing to the achievement of their main objectives.

The small sample size of the present study is a clear limitation. Another aspect that requires further attention is the maintenance of changes over time, and the effects on academic performance and students’ general wellbeing, as well. However, as a pilot and explorative research, these results allow us to continue with the aim of implementing more ambitious experiments and longitudinal studies in the near future.

References Arntz, A., Tiesema, M., & Kindt, M. (2007). Treatment of PTSD: A comparison of imaginal exposure with and without imagery rescripting. Journal of Behavior Therapy and Experimental Psychiatry, 38(4), 345-370.

Beck, B. L., Koons, S. R., & Milgrim, D. L. (2000). Correlates and consequences of behavioral procrastination: The effects of academic procrastination, self-consciousness, self-esteem and self-handicapping. Journal of Social Behavior and Personality, 15(5), 3.

Riva, G., Baños, R.M., Botella, C., Wiederhold BK & Gaggioli, A. (2012). Positive technology: using interactive technologies to promote positive functioning. Cyberpsychol Behav Soc Netw. 15, 2, 69-77

Botella, C., Baños, R. M., Villa, H., Perpiñá, C., & García-Palacios, A. (2000). Virtual Reality in the Treatment of Claustrophobic Fear: A Controlled, Multiple-Baseline Design. Behavior Therapy, 31, 583595.

Bullock, G., Newman-Taylor, K., & Stopa, L. (2016). The role of mental imagery in non-clinical paranoia. Journal of Behavior Therapy and Experimental Psychiatry, 50, 264-268.


Bunge, E., López, P., Mandil, J., Gomar, M., & Borgialli, R. (2009). actitudes de Los teraPeutas argentinos Hacia La incorPoración de nueVas tecnoLogías en PsicoteraPia. Revista Argentina de Clínica Psicológica, 18(3), 209-216.

Burns, L., Dittman, K., Nguyen, N.-L., & Mitchelson, J. (2000). Academic procrastination, perfectionism, and control: associations with vigilant and avoidant coping. Journal of Social Behavior and Personality, 15(5), 3546.

Distéfano, M. J., O’Conor, J., Mongelo, M. C., & Lamas, M. C. (2015). Tecnología positiva. El uso de la tecnología para mejorar el bienestar personal y las interacciones sociales. Psicodebate, 15(1), 93–112.

Eells, T. D., Barrett, M. S., Wright, J. H., & Thase, M. (2014). Computer-assisted cognitive–behavior therapy for depression. Psychotherapy, 51(2), 191-197.

Ferrari, J., Johnson, J., & McCown, W. (1995). Procrastination and Task Avoidance - Theory, Research, and practice. New York: Plenum Press. Retreived from

Foa, E. B., & Kozak, M. J. (1986). Emotional processing of fear: exposure to corrective information. Psychological bulletin, 99(1), 20.

Foa, E. B., Dancu, C. V., Hembree, E. A., Jaycox, L. H., Meadows, E. A., & Street, G. P. (1999). A comparison of exposure therapy, stress inoculation training, and their combination for reducing posttraumatic stress disorder in female assault victims. Journal of consulting and clinical psychology, 67(2), 194.

González-Brignardello, M.P. & Sánchez-Elvira-Paniagua, A. (2013). ¿Puede amortiguar el engagement los efectos nocivos de la procrastinación académica? [Can Engagement buffer the harmful effects of Academic Procrastination?]. Acción Psicológica, 10, 115-132. Recuperado de:

Holmes, E. A., Geddes, J. R., Colom, F., & Goodwin, G. M. (2008). Mental imagery as an emotional amplifier: Application to bipolar disorder. Behaviour Research and Therapy, 46(12), 1251-1258.


Klassen, R. M., Krawchuk, L. L., & Rajani, S. (2008). Academic procrastination of undergraduates: Low selfefficacy to self-regulate predicts higher levels of procrastination. Contemporary Educational Psychology, 33(4), 915-931.

Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106(1), 121-131.

Ministerio de Educación, Cultura y Deporte. (2015). Datos y Cifras del sistema universitario español. Curso 2014-2015. Recuperado a partir de

Norcross, J. C., Hedges, M., & Prochaska, J. O. (2002). The face of 2010: A Delphi poll on the future of psychotherapy. Professional Psychology: Research and Practice, 33(3), 316.

Ozer, B. U., Demir, A., & Ferrari, J. R. (2013). Reducing Academic Procrastination Through a Group Treatment Program: A Pilot Study - Springer. Recuperado a partir de

Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36-48.

Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist, 37(2), 91– 105.

Pintrich, P., & de Groot, E. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. Salanova, M., Shaufeli, W.B., Llorens, S., Peiró, J.M. & Grau, R. (2000). Desde el “burnout” al “Engagement”: una nueva perspectiva?. [From the “burnout” to “Engagement”: A nee perspective?]. Revista de Psicología del Trabajo y de las Organizaciones, 16, 117-134.


Sánchez-Elvira Paniagua, A.(2014). ¿Cómo iniciarse con éxito en el aprendizaje en línea?: la experiencia de la UNED en el entrenamiento de estudiantes autorregulados. En F.Ramos Prado y C.Rama (Eds) Los recursos de aprendizaje en la educación a distancia. Nuevos escenarios, experiencias y tendencias (pp.144-173). UAP Virtual Educa. Lima: Fondo Editorial.

Sánchez-Elvira Paniagua, A. & González Brignardello, M.P.(2014). Analyses of the impact of positive vs. negative personality profiles in student’s learning strategies, wellbeing and performance. 7th European Conference on Personality Psychology. Amsterdam: Holanda, 1-4 of july 2014.

Sánchez-Elvira-Paniagua, A., Fernández, E. & Amor, P. (2006a). Self-regulated learning in distance education students: preliminary data. En A. Delle Fave (Ed.), Dimensions of Well-being: Research and Intervention, (pp. 294-314). Milan, Roma: FrancoAngeli.

Sandín, B., Chorot, P., Lostao, L., Joiner, T. E., Santed, M. A., & Valiente, R. M. (1999). Escala PANAS de afecto positivo y negativo: validación factorial y convergencia transcultural. Psicothema, 11(1), 37-51.

Seligman, M. & Csikszentmihalyi, M. (2000). Positive Psychology: An introduction. American Psychologist 55(1) 5-14.

Schouwenburg, H. C. (2004). Procrastination in Academic Settings: General Introduction. En H. C. Schouwenburg, C. H. Lay, T. A. Pychyl, & J. R. Ferrari (Eds.), Counseling the procrastinator in academic settings. (pp. 3-17). Washington, DC US: American Psychological Association. Recuperado a partir de

Simpson, O. (2012, 3ª ed.). Supporting Students for Success in Online and Distance Learning. London: Routledge

Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling psychology, 31(4), 503.

Steel, P. (2007). The nature of Procrastination: A Meta-Analytic and theorical Review of Quintessential SelfRegulatory Failure. Psychological Bulletin, 133(1), 65-94.


Conference Proceedings The Online, Open and Flexible Higher Education Conference Hosted by Università Telematica Internazionale UNINETTUNO, 19-21 October 2016

Enhancing European Higher Education; “Opportunities and impact of new modes of teaching”

The Online, Open and Flexible Higher Education Conference 2016 - Proceedings

Enhancing European Higher Education “Opportunities and impact of new modes of teaching” Overview of papers on enhancement of European Higher Education as presented during the Online, Open and Flexible Higher Educaiton Conference in Rome, October 2016 Editors George Ubachs | Managing director EADTU Lizzie Konings | Logistics Project Officer EADTU EADTU, October 2016

ISBN: 978-90-79730-25-4

Copyright © 2016 European Association of Distance Teaching Universities and the authors. All rights reserved. Disclaimer: No part of the material protected by this copyright may be reproduced or utilized in any form or by any means, without the prior written permission of the copyright owners, unless the use is a fair dealing for the purpose of private study, research or review. The authors reserve the right that their material can be used for purely educational and research purposes.


Ulriksen, L., Madsen, L. M., & Holmegaard, H. T. (2010). What do we know about explanations for drop out/opt out among young people from STM higher education programmes? Studies in Science Education, 46(2), 209-244.

Watson, D., Clark, L.A. & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063-1070.

Woodley, A., & Simpson, O. (2014). Student Dropout: The Elephant in the Room. Online Distance Education: Online distance education: Towards a research agenda, 459-484.

Zimmerman, B.J.(2002). Becoming a self-regulated learner: an overview. Theory into Practice. v41. 64-71