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BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 2. Frequency of principal strong indices related to 102 'strong articles'

We ordered the obtained data for the identified strengths and weaknesses. This was done by giving a point for each presence of an index, or not giving it for its absence. Thus, we quantified a total of 63 strengths and 91 weaknesses. In addition to finding a great diversity of views captured in the selected texts, the primary data analysis allowed us to set the parameters with the highest frequency. So, the most important for strengths is flexibility representing 29.7% of the total of studied bibliography and an occurrence frequency of 13.6% from the total of identified strengths (Figure 2).

BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 3.Frequency of principal week indices related to the 101 'week articles'

Reduced social interaction is the most important for weaknesses, representing 9.4% of the total of studied bibliography and an occurrence frequency of 5.5% (Figure3). A significant frequency difference of the weaknesses indices compared to the strengths indices was observed. If the top 5 strengths have frequencies ranging between 13.6% and 7%, none of the weaknesses has an occurrence frequency over 6%, all barely ranging between 5.5% and 3.4%, relative to the middle of strengths frequency range. For the practice of e-learning educational services, this may show either a still insufficient detection or theoretical analysis of weaknesses, or, indeed, the superiority of these services.

BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 5. Frequency of common indices related to 102 'common articles'

An additional and very interesting perspective is provided by the analysis of common indices (ambivalent), based on the 102 works-common articles type, and their frequencies (Figure 5).  The data reading indicates 13 ambivalent indices as percentages in descending order: 1. flexibility, 19%; 2. interactivity, 15.4%; 3. cost, 13%; 4. accessibility, 12%; 5. time, 7%; 6. anxiety/reduce social impact, 7%; 7. usability, 6%; 8. connection, 4%; 9. develop skills, 4%; 10. responsibility, 4%; 11. quality, 4%; 12. diversity, 3%; 13. delivery, 1% (Figure 5).

BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 4. Frequency of strong/weak indices related to the 102 'common articles'

Based on the data presented in Figure 2 and Figure 3 and carrying out a comparative and cumulative analysis to identify ambivalent strengths and weaknesses, the 102 ambivalent strong/weak indices related to the 'common articles' in Figure 4 were highlighted.  Their analysis shows that: 1. There are at least 13 ambivalent indicators identified in the studied literature (Table 1); 2. The strengths weight is 62% while the weight of weaknesses is 38.9%, resulting in a pretty big difference in favor of underlining and supporting the strengths, 23.1% more than in favor of weaknesses. These data indicate a significantly higher perception and approach in favor of appreciating the strengths of e-learning educational services, even in the case of their ambivalence. 3. In this context, the data illustrate the following three cases: 3.1. a huge gap between the perception and the interpretation of an index as strength and as weakness (e.g., flexibility is regarded 7.5 times more a strength rather than a weakness). It can be seen that this category of indices definitely belongs to strengths, acknowledged and validated by a large number of studies. In relation to these, efforts will be made for the development, improvement, elevation and obtaining superior parameters. 3.2. a relative correspondence between the perception and the interpretation of an index as strength and as weakness (e.g., the time required to design and implement educational services is considered a strength at a rate of 3.2% and a weakness at a rate of 3.3%). It results that this category of indices has to be studied thoroughly and watched in experimental studies, to replace the uncertainty area in their analysis, to determine which their area of predominance is, to what extent their identified limits and shortcomings have been reduced to allow their conversion into strengths or not; 3.3. a very large gap between the perception and interpretation of an index as a weakness and as a strength (e.g., lack of instructional delivery is considered as being a weakness 20 times more than a strength).This shows that this category of indices comes into focus as weaknesses which need to be analyzed, studied and experimented in order to reduce their negative impact.

BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 5. Emotional flow after 3 hours of graphical editing

To resume, we started with a product interface, found a way to determine two opposite states, than used that way to map user interaction with the product and determine the emotional answer to that interface. The results can then be used to improve product design, to elicit certain emotional responses, etc. For example, in this case, due to the rapid movement (anger) patterns in the aligning phase, a layout that minimizes this can be developed, using a shortcut menu or dynamically appearing guidelines. Furthermore, the user interface can be imagined to be able to learn working patterns and shift the shortcut menu from an aligning menu, if it detects anger patterns, into a color/shape picking menu, if it detects relaxation patterns, therefore assessing the emotional impact and improving the design of the interface in the same time.

BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 2. Detailed view of the individual response

The application that measures the way the user interacts with the PC mouse simulates a simple strategy game. The goal is to choose between different objects in random pattern a particular one. The learning curve of the game is designed to be very fast in order for the subject to quickly understand all the principles and thus the results collected during the test to not be influenced by accommodation to the mechanics. Each 77 individual response were evaluated using the mouse acceleration (reaction) and the number of mouse clicks as indicators for one’s emotional state. Simply plotting acceleration over time provides little information; reaction seems to be very chaotic, and somewhat similar between the two states (Figure 2).

BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 4. Mouse trajectory – images taken from http://iographica.com 4 hours in Photoshop (left) vs. 4 hours in Eclipse (right)

Studies from different fields (Arroyo & Wei, 2006), (Mäkiaho & Poranen, 2012), (Lockton, Harrison, Cain, Stanton, & Jennings, 2013), (Seelye, et al., 2015), (Hehman, Stolier, & Freeman, 2014) have shown that mouse trajectory and clicks are environment (application, device) and user specific, especially if mapped over a long working session (Figure 4).

BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 1. Science branches involved in translating product features to human needs

Approach notwithstanding, designing product features involves basically a two-step process: (a) detecting and recognizing emotional information and (b) exhibiting a suitable reaction to the previously considered input. Since needs are reflected in the emotional impact, a method to measure the emotions is necessary (Abraham & Michie, 2008) to correctly assess the impact. Referring strictly to a software product, in order to implement the capability to sense the users’ emotional state, the first step should be developing an affective database (Tao & Tan, 2005), to allow correct identification of the affective status. This results in a translation between the affective status of the user and the computer, therefore allowing the program to process the user emotion just like any other input, successfully “digitizing” emotion.

BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 3. Average values in acceleration and clicks

However, when looking at the median values of acceleration and clicks, a clear difference appears between the states: more clicks for relaxation, higher acceleration for stress (Figure 3).

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 9. Illustration of three different wavelets with three waves that have high amplitude values, as indicated by the dashed red circles

The mother wavelet of Coiflet 5 contained triple-high oscillation amplitude (i.e., Figure 9a). We considered that this mother wavelet was inappropriate for our data because overall our data possibly contained only a few matches with the mother wavelet of Coiflet 5. Moreover, the Symlet 10 (i.e., Figure 9b) and 20 (i.e., Figure 9c) also provided supportive results that were lower than others in ANNSVM_WLHT because their mother wavelets also had a similar shape as that of Coiflet 5. For similar reasons, the Haar wavelet was not proper because it is a step function.

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 9. Proportion of indicators used in the case studies

Figure 9 presents the indicators used for evaluating the effectiveness of mobile and ubiquitous learning practices. Learning achievements (64%) and perceived usefulness (56%) were the two most frequently used, followed by motivation (26%), ease of use (26%) and satisfaction (24%). cognitive load (12%), system usage (8%), self-efficacy (6%) and social engagement (2%). Those indicators were usually adopted in the studies using qualitative methods for data collection. The results suggest a possible relationship between the data collection methods and the indicators. The choices of indicators represent, in principle, how the effectiveness of mobile and ubiquitous learning practices can be most appropriately evaluated and presented using particular study methods.

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 8. Functions of the mobile devices used in the practices (Note: Each case could involve the use of more than one function.)

Figure 8 captures the functions of mobile devices used in the practices. The results show that tailor-made applications for specific practices were most common (74%), followed by the use of a speaker (32%) and a camera (26%), where learners had to listen to audio materials using speakers or access online information by scanning QR-codes through cameras. In the various practices, other functions were also used, such as messaging (16%) for interacting with diverse parties and GPS (14%) for outdoor learning activities. For the practices using older models of mobile devices without cameras, tools such as an RFID reader (8%) were used for accessing information via communication tags.

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 5. Level of intervention of the studies

Figure 5 illustrates the levels of intervention of the studies. Most studies concentrated on the course level (76.9%) and some on either the programme level (13.5%) or the institutional level (9.6%). Two studies were conducted at more than one level. These results supplement the above number of participants where most of the studies were conducted on a small scale.

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 6. Purposes of using mobile devices for learning

Figure 6 shows the purposes of mobile and ubiquitous learning in the studies. A majority of the cases were multi-purpose (54%). Those with a single specific purpose were focused on “practice or revision” (34%) and “knowledge acquisition” (10%). There was a case where the application was to help new students to become familiar with the campus and teach them about the use of the facilities.

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 1. Geographical distribution of the case studies

The 50 cases of mobile and ubiquitous learning practices covered 14 countries/regions, including China, Japan, Taiwan, Korea, Sri Lanka, Turkey, Spain, Greece, the Netherlands, Britain, Australia, New Zealand, South Africa and the USA. Figure 1 shows the geographical distribution of the cases. Among the 50 cases, 70% were conducted in Asia, 16% in Europe, 4% in Oceania, 4% in Africa, and 2% in North America. Therefore, the results of this study represent more of the situation in Asia.

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 7. Level of interactivity of the studies

Figure 7 presents the level of interactivity in using mobile devices. Sixty percent of the cases involved only one-way access for information either online or offline, while 8% involved social interaction with information exchange among learners. Also 32% of the cases included both levels of interactivity in learning.

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 4. Number of participants in the studies

Figure 4 shows the number of participants in the studies, with the majority being on a small scale, having less than 100 participants (72%). Twenty percent of the studies involved over 100 but less than 1,000 participants, and only two cases included more than 1,000 participants (4%).

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 2. Data collection methods of the studies (Note: A study may involve more than one method.)

Figure 2 shows the data collection methods applied in the studies. Surveys were used in most of the selected cases (94%), followed by interviews (38%) and experiments (34%). Apart from these approaches, observation (12%), field materials (8%), video recordings (6%) and discussion threads (2%) were also used in some of the studies. Thirty of the cases involved the use of more than one method, mostly combining a survey and another one or more method. These suggest that most of the studies involved quantitative data at least in part.

Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 3. Education level of the studies

Figure 3 presents the education levels of the mobile and ubiquitous learning practices. Most studies took place at the tertiary level (66.7%). Also, 25.5% of the studies were conducted at the primary school level and 7.8% at the secondary school level.