His model comprises three main phases pre-actional, actional, and post-actional phases. The decisions made in the pre-actional phase put the model into practice. During the pre-actional phase, the agents who intervene the teacher and students in the educational setting make periodical re-evaluations reappraisals about the requested demands, supports and resources that they will receive to meet these demands and to overcome the barriers and difficulties, etc.
The decisions made at the start of the educational process will have more chances of fluctuating in a long instructional segment e.
Teaching with technology
Finally, the agents involved the teacher and students evaluate and reflect on the obtained outcomes reflectional phase and they self-regulate to become more efficient in the next instructional segment task, unit, thematic block, course, etc. Despite it being a model that simultaneously deals with the teacher and students, we center basically on students for simplification and space reasons as students have the element that has generated most of the research conducted to date within the MOCSE framework. A summary of the theories, models and principles that feed the Educational Situation Quality Model and the specific contributions considered from each one.
Continued poor performance and negative performance feedback produce frustration and may consequently affect future outcomes Weiner, It is expected that a background of success will contribute to a more positive perception than a background of failures. Based on these assumptions and findings, we defend that the variables which refer to student success and failure attributions, as well as affective memories about past academic experiences, should be taken into account.
The same recommendation can be made for teachers. Demands can become stressing if they require a great deal of effort for the subject to achieve it. Subsequent research Lorente et al. Adapting and applying the JD-R model to the school context implies identifying what the specific demands and resources are for teachers and students in the school context. The learning environment quality depends to a great extent on the simultaneous presence of environmental challenge basically through educational demands and the environmental support provided Shernoff et al.
One way of create a challenging classroom environment is designing novel and attractive demands. Demands can become stressing if they require a great deal of effort for the subject to meet them. When applying this theory to the school context, we consider that the demands that are difficult to be perceived by students to pass a specific subject may affect their level of stress and engagement the same reasoning is also plausible for teacher demands.
A moderate difficulty of demands, not too high, not too low, is recommended. The demands that students have to meet studying demands in order to pass a specific curricular subject e. The information from the evaluation system, such as tasks to do, level required to pass the subject, evaluation criteria, etc.
Similarly, demands under teacher responsibility and their perceived difficulty may also influence teacher stress or engagement depending on their level of difficulty because, as we state before, demands can become stressing if they require a great deal of effort for the subject to meet them. Following the classification proposed by Schwarzer and Greenglass , support can be internal or external. External support comes from the workplace, basically from administrative, social and didactic resources.
Administrative support for teachers comes from school administrators e. Social support may come from either inside or outside the school. Referring to inside the school, supports for students come from teachers, peers, etc. Referring to outside the school, supports for students basically come from family and friends, whereas supports for teachers come from family and partners. Didactic resources refer to learning tools for students that are provided by teachers e. The learning environment quality depends, to a great extent, on the simultaneous presence of the challenges offered and on the supports provided Shernoff et al.
According to previous studies Patrick et al. Theoretically, some authors have distinguished between general and specific emotional support Federici and Skaalvik, A number of studies have centered on teachers being emotionally supportive, and have found emotional support to be related to high levels of intrinsic motivation Katz et al.
The Use of Technology – In Education and Teaching Process
According to previous studies Suldo et al. This instructional help includes teacher behaviors, e. A number of studies have centered on teachers as being instructional supportive, and have found instructional support to be related to social, behavioral and academic outcomes Malecki and Demaray, However, fewer studies have centered on instructional support than on emotional support. An effective teacher knows how to adapt the type of supports to the different kinds of tasks to be done and the demands to be met. Peers can also provide help and support in developing social and academic competences Wentzel et al.
Therefore, peers support should also be taken into account. Support for students can be provided beyond the classroom context, so the support that comes from outside the classroom e. Jelas et al. The data analysis was conducted by structural equation modeling. Family members who provide academic e. As seen above, external support comes basically from the classroom and family context.
These are examples of questions related to personal identity. Positive self-beliefs e. It is important to take into account the accuracy of perceptions; i. All this information will help us to select which personal variables are to be considered in the future. Before ending this section, we wish to underline some important ideas.
Second, the perception which students have formed of the educational setting, in terms of demands and support, at the beginning of the course is very important because it may condition their way of learning and engagement from the beginning. How and when to provide these supports should be considered in teacher training programs. Finally, we wish to indicate that all these ideas focus on students, but can also apply to the teacher. The main student supports come from the classroom and family context. According to Shernoff et al.
One axis ranges between high supports-low barriers the positive pole and low support-high barriers the negative pole. The other axis ranges between attractive-meaningful demands positive pole and unpleasant-meaningless demands negative pole. Based on the above rationale, we hypothesize that the perception of demands and support variables at the beginning of an instructional segment e. The conceptualization of intention to learn will be explained in the next sections.
As can be seen, quadrant I predicts the maximum activation of intention to learn; in contrast, quadrant III predicts the minimum activation of intention to learn. High difficult demands predict an expected stressful process during the course. Finally, we wish to point out that the perception of demands and support variables can also be influenced by prior academic experiences viewed by students in either the same educational setting or similar precursor courses e. A background of success will contribute to the perception of positive demands and support variables; in contrast, a background of failures will contribute to a negative perception.
Ways of students perceiving the input variables and hypothesized predictions to intention to learn according to two bipolar orthogonal axes: One axis ranging between high supports-low barriers and low supports-high barriers, and the other axis ranging between attractive-meaningful demands and unpleasant-meaningless demands. Intention to learn is a complex construct in which multiple factors are involved.
For us, intention to learn has the same meaning as motivation to learn. Intention is considered the immediate antecedent of action. During the first process, initial wishes, desires and hopes are evaluated in terms of their chances of being fulfilled. During the second process, wishes, desires and hopes are transformed into goals.
The latter represents the process through which learners specify and make a decision. The indicators selected for both dimensions derive from the three dominant theories in the contemporary literature of achievement motivation, all of which are grounded in a socio-cognitive perspective of motivation: the expectancy-value theory Wigfield and Eccles, ; Eccles and Wigfield, , Attribution theory Weiner, and the achievement goal theory Dweck and Legget, ; Nicholls, ; Ames, ; Wigfield and Eccles, Although this concept seems relatively simple, it is not so because it has many conditioners.
An object can have an intrinsic, extrinsic and instrumental value as a step to fulfill a longer term goal. Eccles et al. They defined attainment value as the personal importance of doing the task well. Intrinsic value is the enjoyment that the individual gets from performing the activity related to process expectancy. Finally, these authors identified cost as a critical component of value, which was conceptualized as a negative determinant in engaging in a task due to, e.
Prior studies Cole et al. In the same vein, Miller and Brickman argued that students only make their best effort and spend a substantial amount of time on mastering an academic task if they perceive it to be important and useful for them in the future. If from the beginning of the course students and the teacher predict that their wishes, hopes and desires are going to be met in the current educational situation, they will transform them into goals learning goals.
Bandura argued that most human motivation is cognitively generated. Bandura also indicated that the two basic capacities that largely explain human behavior are their prediction and self-regulation capacity. Individuals predict the consequences of future actions through prediction capacity.
This capacity allows people to feel motivated and to regulate their actions in advance self-regulation. Predictions can be associated with underlying motivational processes Bandura, Motivational processes are activated by the learner and the teacher with a specific subject at the beginning of the course. Expectancies for success, efficacy expectancies and outcomes expectancies were the more widely used in this tradition. We distinguished three components process expectancy beliefs: expected teacher—student, peer—student, and subject-student interaction.
They refer to the feelings or affective reactions that students expect to experience in the new course, derived from the teacher—student, peers-student and subject—student relationships. Indeed, nobody starts a task if they do not expect be feel well during the performance process Pintrich and De Groot, First, theorists centered on the locus of control concept Rotter, by distinguishing between internal and external locus of control.
The locus of control dimension refers to whether causes are located internally internal locus of control or externally external locus of control of the individual. The stability dimension refers to whether causes change over time or not stability vs. Controllability distinguishes the causes that one can control e. This dimension refers to when students and the teacher make the decision, based on the previsions made from the anticipatory cognitive motivators, about how they will face the current situational setting. Thus, the Achievement Goal Theory Dweck and Legget, ; Nicholls, ; Ames, ; Wigfield and Eccles, is proposed to explain and operationalize this dimension.
Three types of achievement goal that have been usually studied are mastery, performance-approach and performance-avoidance Skaalvik, ; Midgley et al. The students who adopt a performance goal are concerned about the demonstration of competence shown by others. The students who adopt performance-avoidance goal wish to avoid social judgments and humiliation.
The above classification can be completed with two more types of goals introduced by Alonso Tapia named self-worth and social recognition goals. Students who adopt the former want to get proud of their own performance. Students who adopt the latter type wish to obtain social recognition from others, such as teacher, parents, etc. Based on the two classifications exposed above, we have distinguished three broader groups of achievement goals according to two parameters: type of motivation intrinsic vs.
Type I goals, based on intrinsic motivation. In this case the most important point for students is to improve their skills and progress e. Type II goals, based on extrinsic motivation and positive reinforcement. In this case the most important concern for students is to demonstrate their competence in obtaining social recognition or another reward e.
Type III goals, based on extrinsic motivation and negative reinforcement. In this case the most important point for students is to avoid humiliation and embarrassment, and to protect their self-esteem e. According to King and Mclnerney mastery and performance goals differ in terms of how competence is defined. Finally, there is also the possibility that some students do not pursue any of the aforementioned goals.
This occurs when students are not motivated to learn and make the minimum effort, or even try to avoid learning, which reflects passivity or inaction. Work avoidance represents the absence of an achievement goal Elliot, For a more extensive description, see the study conducted by King and Mclnerney , who examined the structure, antecedents and consequences of the avoidance goal construct. To summarize, two main ideas are pointed out.
First, the maximum activation of intention to learn is achieved when students believe that the subject is worth pursuing, make positive forecasts and adopt domain-focused goals. The main objective of these students is to master the subject, to learn and to progress. They are characterized by taking on difficult challenges, by striving to learn, and by getting actively involved active coping in the T—L process.
Second, we use two dimensions to evaluate intention to learn or motivation to learn , a the first dimension indicates the subject worth pursuing and chances of fulfillment, b the second dimension indicates the achievement goal-setting; i. Students may be motivated to master the subject, obtain reinforcement, get good grades, demonstrate their worth, etc. The quality concept is explained in detail in the next section. Resulting interactions between intention to learn and intention to teach, and the expected predictions in T—L process quality. Motivation evolves gradually through a complex psychological process that involves basically initial goal setting, intention activation and planning pre-actional phase before action implementation.
We are aware that the components we propose to evaluate intention to learn are a necessary, but not sufficient, condition to completely explain action. We wish to underline that intention to learn and intention to teach does not remain constant during the T—L process. Finally in long-term processes, motivation to do something basically involves intention activation, making decisions and action implementation in this sequential order, although these steps follow on from each other almost simultaneously.
In short, the first two components of the model appraisal phase and intention activation , plus the educational action plan, represent the pre-actional decisional phase, and are responsible for learner and teacher decisions about how to deal with a new educational process. Students decide in this phase if they first want to engage in order to master the school subject or not.
Basically, there are three time points when the agents involved in the learning-teaching process make important decisions about the course, which are also known as macrodecisions microdecisions are made about the theme or task. The most important time point is the period at the beginning of the course, but the time points that correspond to the second and third trimesters are also important in Spain, school courses last three terms, and each term lasts 3 months.
The decisions made at these two time points are mainly triggered by knowledge about a new element: the results that students obtain when they end each trimester, which are provided on a report card. The actional phase represented by component 3 covers those variables relating to the teaching and learning strategies undertaken by the teacher and students to achieve learning objectives.
Specifically, it refers to how the three key elements teacher, content, and learner interact during the T—L process conducted in the classroom with a specific subject matter. Following Vigotsky, we can state that the teacher and students interact through content instrumental mediator , while students interact with content mainly through the teacher social mediator. The interaction of the three key elements teacher—content—students is crucial for quality learning.
Based on this assumption, in this section we present an integrative approach of the T—L process, operationalized and defined by specific quality indicators. For more details, see Harvey and Green Applying both notions to education implies following during the T—L process both the teacher and students the principles that psycho-educational research has demonstrated as being effective to generate as many changes as possible in students in the cognitive and socio-affective domains by taking the fixed learning objectives as a reference to conduct instruction.
Taking the interaction of the three key elements teacher, content, and students as starting point, a high quality T—L process first requires the active and simultaneous participation of the three key elements throughout the T—L process. Nevertheless, each has a different degree of responsibility in the different phases that make up the process Rivas, , So, cognitive and physical activity and the interaction among participants in a specific curricular subject are an essential condition of the quality T—L process. The second requirement of is to provoke the most marked transformation possible among students in their cognitive and socio-effective domains.
What do we mean by “technology”?
The quantity and quality of the academic results acquired in terms of changes and transformation are related to T—L process efficiency. Finally, the third requirement refers to the teacher having to enjoy teaching and students having to enjoy learning. The latter can only be possible if the teacher and students experience positive emotions while implementing the T—L process. Positive emotions in the classroom are related to the psychological health of both teachers and students.
These three characteristics active, efficient and healthy influence each other. Below we explain in more detail the three traits that best define a high quality T—L process. Active learning is usually defined by authors as the learning that requires students engaging cognitively and meaningfully with materials Bonwell and Eison, to get involved with the information presented analyzing, summarizing evaluating rather than just passively receiving it King, From a motivational perspective, active learning focuses on either the precursor attitude or the interest in getting involved in learning tasks.
From a behavioral perspective, active learning focuses on student actions, such as how often students attend class or do their homework, etc. Finally from an emotional perspective, active learning focuses on affective reactions to teachers, peers, etc. In the current work, active learning is used as cognitive and behavioral engagement.
On the contrary, if they perceive a threatening environment they will wish, mainly, to protect themselves from negative emotions, and will therefore very likely develop passive strategies that focus on emotion e. It would be desirable for both students and teachers to adopt active coping strategies problem-focused coping , engaging and striving to overcome the problems or challenges that may arise throughout the educational process.
Teachers can promote active learning by creating a more active learning environment Bonwell and Eison, ; Fink, by, for example, designing significant lessons and activities that overcome passive learning. A T—L process is considered efficient when it provokes the most marked transformation possible in students in their cognitive and socio-effective domains.
This will be possible if the psycho-educational principles and specifications derived from the empirical research proposed by the literature are followed by teachers and students. Demands for students are related to learning objectives, tasks and contents. Therefore, effective actions and strategies from the teacher and students are needed to meet learning objectives.
It is important that the teacher enjoys teaching and that students enjoy learning. This means having to focus on the process as much as on the results, or even more. When focusing on the educational situation as a stage where the T—L process takes place, it is important to study and identify the process elements that could affect positively or negatively the psychological health of teachers and students. Traditionally, the research carried out in this field has focused more on studying negative emotions teaching discomfort, stress, burnout, etc. However, this approach has changed in recent years with the rise of positive psychology.
From the psychological point of view, we understand a healthy educational process as one that produces in agents or participants the teacher and students positive emotions; on the contrary, if the emotions experienced by the involved subjects are mostly negative, it is considered an unhealthy process be aware that triggers can also originate from the external contexts directly connected to the educational situation, such as family and school. Teaching is highly emotional work Marchesi, given the constant interactions among the three key elements teacher, content and students throughout the T—L process.
Based on MISE, we particularly stress the importance of the Personal Interactions dimension dimension three as most of the emotions experienced by the agents involved the teacher and students are activated in this phase. A healthy educational process will provide well-being for the teacher and students which, in turn, will affect the quality of education.
Finding the instructional keys that can contribute to teacher and student well-being is a challenge that we are currently working on. Finally, others have a shared responsibility. Currently, we investigate the contribution of the MISE-4D indicators to the three main features active, efficient and healthy that define the quality of the T—L process. The correlational analyses provided interesting clues to identify which MISE-4D variables are involved in the quality of the T—L process in the university context.
In order to find regularities in similar educative situations subjects and at the same educational level, further research is needed to identify the role played by the MISE-4D variables in each specific context. The product phase refers to learning outcomes. Student achievement and satisfaction are two of the most important learning outcomes of students, and are also considered key indicators of education quality.
We understand learning as a change in students to move from an initial state to a final one Rivas, , Traditionally teachers have placed more emphasis on the cause of quantitative and information-conceptual changes rather than on more useful and effective formative changes to solve problems and to make decisions.
However in recent years, a paradigm shift in this direction has been promoted by experts and educational leaders. The final goal of all T—L processes is to accomplish the desired results specified in terms of learning objectives. Satisfaction is considered both an outcome of the T—L process, and an important requirement for successful learning Sinclaire, It would be desirable for a sense of satisfaction to be perceived by both the teacher and learners.
Students should experience satisfaction with the results they obtain and with the T—L process followed. The role played by the teacher in terms of the instructional and emotional supports provided may contribute to increase student satisfaction enjoyment and accomplishment. Previous research has identified a number of factors that contribute to student satisfaction, among which we wish to highlight interaction Wu et al.
Therefore, the first action to perform aims to evaluate intention to learn variables at the beginning of the T—L process because this will provide teachers with valuable information about the extent to which students will be engaged in studying and working on a specific subject. If the evaluated intention to learn is deficient, it would then be recommendable to identify the demands and support variables responsible for this shortcoming.
As we state before, in order to increase intention to learn, attractive and meaningful demands should be designed, and emotional and instructional supports from teachers, peers, parents, etc. There are basically three time points in which the agents students and their teacher involved in the T—L process make decisions about the course, which we call macrodecisions microdecisions are made about the theme or task.
The most important time point is the period when the course begins, but the time points that correspond to the start of the second and third trimesters are also key. The decisions made at both these time points are triggered mainly by knowledge of a new element: the results that students obtain at the end of each trimester, provided on a report card. Furthermore, it should be pointed out that the MISE-4D is a versatile model as it allows indicators to be adapted to a specific educational setting, while the core model structure is preserved dimensions and indicators ; i.
So MISE becomes a valuable tool for teacher formative evaluation as it provides them with empirical data from teacher and students perspective throughout the T—L process. Finally, the learning outcomes and satisfaction experienced about the outcomes and the process followed should be considered and evaluated. The aim of this evaluation is to first know to what extent the learning objective has been fulfilled and, second, to know the student satisfaction reported of both the results they have obtained and the T—L process followed.
The same procedure and actions can be implemented at the school level. The empirical data obtained through MOCSE procedures can provide the scientific basis to design effective programs for schools adapted to different levels of education and subjects. It is also a useful tool that can be employed by policy makers and educational leaders to design prevention and intervention programs to increase learning outcomes and satisfaction at schools. MOCSE is an instructional model that provides a conceptual framework capable of explaining the functioning of an educational setting in an integrative way.
To design the model, contributions made from several relevant psycho-educational theories have been taken into account. This proposal introduces a new perspective into the existing literature that will allow researchers to make progress in studying educational setting functioning. Kollar, H. Haake Eds. Scripting computer-supported collaborative learning: Cognitive, computational, and educational perspectives.
New York: Springer. Kollar, I. Collaboration scripts—a conceptual analysis. Educational Psychology Review , 18 2 , — Internal and external collaboration scripts in webbased science learning at schools. Mahwah, NJ: Lawrence Erlbaum. Krippendorf, K. Content analysis: An introduction to its methodology. Beverly Hills: Sage Publications. Krippendorff, K.
Reliability in content analysis: some common misconceptions and recommendations. Human Communication Research , 30 , — Kuhn, D. The skills of argument. Cambridge: Cambridge University Press. Kumar, R. Tutorial dialogue as adaptive collaborative learning support. Kupiec, J. Lafferty, J. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Laham, D. Automated content assessment of text using latent semantic analysis to simulate human cognition. PhD dissertation, University of Colorado, Boulder.
Landauer, T. Psychological Review , , — The potential of argument in knowledge building. Human Development , 43 , — Lewis, D. RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research , 5 , — Litman, D. Spoken versus typed human and computer dialogue tutoring.
Luckin, R. Between the lines: Documenting the multiple dimensions of computer-supported collaboration. Computers and Education , 41 , — McLaren, B. Using machine learning techniques to analyze and support mediation of student e-discussions. Proceedings of Artificial Intelligence in Education. O'Donnell, A. Scripted cooperation in student dyads: A method for analyzing and enhancing academic learning and performance.
Miller Eds. Interaction in cooperative groups. The theoretical anatomy of group learning pp. Page, E. The use of the computer in analyzing student essays. International Review of Education , 14 , — The computer moves into essay grading: Updating the ancient test. Phi Delta Kappan , 76 , — Pennebaker, J. The social, linguistic, and health consequences of emotional disclosure.
Wallston Eds. Social psychological foundations of health and illness pp. Malden, MA: Blackwell. Cognitive, emotional, and language processes in disclosure. Cognition and Emotion , 10 , — Linguistic inquiry and word count: LIWC. Mahwah, NJ: Erlbaum. Piaget, J.
The equilibrium of cognitive structures: The central problem of intellectual development. Chicago: Chicago University Press. Roman, N. Politeness and bias in dialogue summarization: Two exploratory studies, in J. Shanahan, Y. Wiebe Eds. Dordrecht: Springer. A framework for robust semantic interpretation. Automatic and semi-automatic skill coding with a view towards supporting on-line assessment. Towards and interactive assessment framework for engineering design project based learning. Interactive conceptual tutoring in atlas-andes.
Moore, C. Johnson Eds. A hybrid text classification approach for analysis of student essays. An evaluation of a hybrid language understanding approach for robust selection of tutoring goals. International Journal of AI in Education , 15 4. Salomon, G.
Individual and social aspects of learning. Review of Research in Education, 23, 1—4. Schegloff, E. Opening up closings. Semiotica , 8 , — Schoor, C. Motivation and processes of social co-construction of knowledge during CSCL. Serafin, R.
Educational technology for teaching and learning
FLSA: Extending latent semantic analysis with features for dialogue act classification. Proceedings of the Association for Computational Linguistics. Soller, A. Modeling the Process of Collaborative Learning. Japan: Awaiji—Yumebutai. Stahl, G. Group cognition: Computer support for building collaborative knowledge. Stegmann, K. Facilitating argumentative knowledge construction with computer-supported collaboration scripts.
Stolcke, A. Dialogue act modeling for automatic tagging and recognition of conversational speech.
proxy.littlelives.com/first-time-shemale-ecstasy-jenna-a.php Computational Linguistics , 26 3 , 39— Teasley, S. Talking about reasoning: How important is the peer in peer collaboration? Resnick, R. Burge Eds. Discourse, tools and reasoning: Essays on situated cognition pp. Tsochantaridis, I. Support vector learning for interdependent and structured output spaces. Proceedings of the International Conference on Machine Learning The affordance of anchored discussion for the collaborative processing of academic texts.
VanLehn, K. Natural language tutoring: A comparison of human tutors, computer tutors, and text. Cognitive Science , 31 1 , 3— Vapnik, V. The nature of statistical learning theory. Heidelberg: Springer. Voss, J. Argumentation in psychology. Wang, Y. A feature based approach for leveraging context for classifying newsgroup style discussion segments. Wang, H. Thinking hard together: The long and short of collaborative idea generation for scientific inquiry. Webb, N.
Peer interaction and learning in small groups. International Journal of Educational Research , 13 , 21— Wegerif, R. A dialogic understanding of the relationship between CSCL and teaching thinking skills. Weinberger, A. Scripts for computer-supported collaborative learning. Effects of social and epistemic cooperation scripts on collaborative knowledge construction.
- A Course in Multivariable Calculus and Analysis (Undergraduate Texts in Mathematics)?
- How can technology help you??
- The Educational Situation Quality Model: Recent Advances.
- The Educational Situation Quality Model: Recent Advances.
Ludwig-Maximilian University, Munich.