Idiopathic pulmonary fibrosis is an interstitial lung disease of unknown etiology with a highly compromised prognosis and a significant mortality rate within a few years of diagnosis. Despite being idiopathic, it has been shown that telomeric shortening could play an important role in its etiopathogenesis. Mutations in telomere-related genes have been identified, but they are not always present despite telomere shortening. On the other hand, this telomeric shortening has been linked to a worse prognosis of the disease independently of other clinical factors, implying it may serve as a biomarker. Idiopathic pulmonary fibrosis (IPF) is the most common form of chronic idiopathic diffuse interstitial lung disease in adults. It is a chronic and progressive disease with a high mortality rate that affects mainly elderly individuals, with an average age of diagnosis of 66 years . IPF is IPF is characterized by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. The epithelial damage is thought to be caused by an aberrant fibrotic response with the deposition of dense material, which reduces lung flexibility and its capacity for gas diffusion .The etiopathogenic mechanisms of this disease are not fully understood, with smoking being one of the main risk factors ,5. Age iIPF is recognized as a rare disease with an incidence of around 10 cases per 100,000 per year in Europe and North America . HoweverThere are two drugs available: Nintedanib and Pirfenidone, and although both have been shown to slow the progression of the disease, neither is able to stop it completely.Telomeric shortening has been linked to IPF in multiple studies ,14. TeloIn recent years, multiple telomere-related genes have been identified that cause telomere shortening and that are associated in significant percentages with IPF. More specifically, mutations in telomere genes have been found in up to 25% of familial cases and 1–3% of sporadic IPF cases. In addition, in sporadic cases not associated with telomere-related gene mutations, shorter telomeres have been observed compared to people of the same age without the disease, with 10% of patients having telomeres as short as those of telomerase mutation carriers. Telomeric shortening has also been observed in some studies in other lung diseases with fibrosing phenotypes, always giving them a worse prognosis.The importance of determining the prognosis of the disease has increased with the study of new drugs for this entity and the expanding eligibility for lung transplantation.The purpose of this review is to summarize the data concerning telomeropathies in relation to IPF.The etiology and pathogenesis of the disease are still unclear, although lung fibroblast and epithelial cell activation, as well as the secretion of fibrotic and inflammatory mediators, have been linked to its development and progression . MoreoveSmoking has always been considered the main risk factor, with a percentage of former or current smokers ranging from 41 to 83%  (p. 1).Exposure to stone, metal, wood, and organic dust has also been suggested as a risk factor ,20.It is believed that gastroesophageal reflux may contribute in the form of microaspirations. However, it is difficult to make associations considering its high prevalence in the general population.From 2 to 20% of patients with IPF have another affected family member ,22,23,24Therefore, although genetic factors represent an important etiopathogenic mechanism, they do not seem to be sufficient or depend, in most cases, on another triggering factor.Nonetheless, it is considered that the greatest risk factor for IPF is having an affected family member.The capacity to regenerate and repair damaged tissue is progressively reduced with age. One of the mechanisms related to the aging process is telomeric shortening. Therefore, in IPF, as in other diseases with premature telomeric shortening, this phenomenon leads to poor tissue repair, in this case of the pulmonary epithelium, and this drives to lung scarring and the development of pulmonary fibrosis.Analysis of familial IPF patients with mutations in telomere-related genes showed diminished telomerase activity and prematurely shortened telomere length. Similar results were found in sporadic patients not carrying any mutations when compared to healthy controls ,27.These findings suggest that telomeric shortening plays a key role in the etiopathogenesis of IPF, which is not fully explained by genetic mutations, at least not in every case.On the other hand, it should also be taken into account that telomeric shortening can be caused by exogenous factors, such as oxidative stress or smoking, but can also be due to increased proliferation of immune cells ,29. TherTelomeres are non-coding repetitive sequences of nucleotides (hundreds to thousands of TTAGGG tandem repeats) found at the ends of chromosomes that protect them from erosion. At each cell division, these repeats are lost, thus shortening the telomeres progressively .Telomere shortening is considered the primary molecular cause of aging since shortened telomeres limit the replicative capacity of stem cells, blocking the regenerative capacity of tissues, and thus causing cellular senescence. This leads to the development of age-related diseases .Telomerase is a reverse transcriptase that is able to elongate telomeres de novo by adding TTAGGG repeats to chromosome ends  after miSeveral recent studies have demonstrated the relationship between telomeric shortening and loss of cellular immune function, known as immunosenescence.When there are mutations in genes related to telomere maintenance, the corresponding diseases are known as telomeropathies. Classic telomeropathies include: Hoyeraal-Hreidarsson syndrome, dyskeratosis congenita, pulmonary fibrosis, aplastic anemia, and liver fibrosis.A variety of experimental and genetic studies support the hypothesis that telomere shortening contributes to the pathogenesis of IPF; however, it does not seem to be enough. In a mouse model with telomerase deletion, no phenotypic changes were seen. However, after several generations, mice with short telomeres developed degenerative diseases with phenotypic changes related to aging ,36.Therefore, telomeric shortening plays a key role in the etiopathogenesis of IPF, but a second hit, such as smoking, viral infections, or gastroesophageal reflux, is necessary to contribute to the onset of IPF.In addition, it is known that telomeric length, which is known to be an inheritable trait from the progenitors , will thIn recent years, there has been growing evidence that genetic factors play an important role in IPF. In addition, the fact that there are several relatives affected by IPF in the same family suggests a genetic mechanism. However, mutations in these genes are not exclusive to familial forms they have also been found in sporadic cases ,39.The genes most frequently affected by the mutations are as follows  :SFTPCSFTPA2On the other hand, there are the surfactant-encoding genes, which are much rarer:There are more familial or sporadic patients with telomeric shortening than with detected mutations; therefore, it is believed that most of the genetic mechanisms remain unknown  (p. 1) aFamilial pulmonary fibrosis (FPF) is considered when there are two or more affected members, and it represents up to 20% of the cases of IPF, depending on the series ,41,42.The demographic and clinical characteristics of patients with FPF are similar to those of patients with sporadic disease, except for their younger age at diagnosis , being dIt exhibits a wide range of pulmonary fibrosis phenotypes, being in most cases idiopathic pulmonary fibrosis. However, there can also be different diagnoses in the same family: hypersensitivity pneumonitis, desquamative interstitial pneumonia, and pleuropulmonary fibroelastosis, among others.Telomere length can be determined by different methods. These include Southern blot, quantitative PCR (Polymerase Chain Reaction), and Flow-FISH (flow cytometry with fluorescence in-situ hybridization), which are the most frequently used since they don’t require a large amount of DNA. It can also be measured with TRF  analysis, STELA , which was designed to measure telomeres on individual chromosomes, and TeSLA (telomere shortest length assay), the newest technique, but it is usually used only for research .In addition, telomere length can be determined in several places: in peripheral blood leukocytes, the most classically studied method, or, as recently described, with an oral swab harvesting epithelial cells from the cheek. There is no standard technique to measure telomere length, but these two techniques are the most accessible. Moreover, telomere length can also be measured in lung epithelial cells, specifically, alveolar type 2 cells. In these cells, it has been determined in different areas of the lung, with no differences found between areas regardless of the typical fibrosis pattern  ,47. SeveOn the other hand, all these tests and, above all, the genetic testing are expensive and/or not always available, so they are not recommended at this time if no genetic disease is suspected. There are certain characteristics of the patients that should make us think of telomeropathies, and therefore we should request further testing .The importance of screening relatives lies in the fact that interstitial alterations have been observed in up to 25% of first-degree relatives of patients with IPF ,51,52,53Inherited short telomere lengths in the absence of telomerase mutations have been reported previously . This coCurrently, there are no guidelines for what to do when a genetic form of IPF is suspected. It should also be noted that the disease does not usually manifest itself, even in inheritable cases, before the age of 40  (p. 2). Accordingly, as mentioned above, due to the limited availability of the tests, it must be carefully considered in which contexts to carry out telomeric/genetic studies. A clear context for doing counselling would be when a mutation in telomere or surfactant-related genes has been detected in the index case. In this case, genetic counseling should be given to first-degree relatives who want it.On the other hand, with the increasing importance of genetics in medicine, it might be reasonable that all individuals with IPF are offered genetic counseling and it seems also essential to counsel future generations of parents with telomeric shortening or related mutations, especially to avoid substances that are harmful to the lung and that have been shown to be particularly sensitive to telomeric dysfunction, like smoking or other toxics .Currently, there are only two drugs approved for IPF: Pirfenidone, which is an antifibrotic agent that inhibits transforming growth factor beta (TGF-b)-stimulated collagen synthesis and decreases extracellular matrix and fibroblast proliferation, and Nintedanib, which is a receptor blocker of multiple tyrosine kinases that collaborate in the synthesis of profibrogenic growth factors. Both drugs are only capable of slowing down the disease without stopping it completely. On the other hand, the incidence and prevalence of IPF have been increasing, as the population ages, and there is an urgent need for improved therapies.There is no specific treatment for IPF patients with telomeropathy. However, in the future, it would be an interesting field to study in order to choose a more personalized treatment for the patient.Newton et al.  measuredIt has also been studied whether to use the maintenance of telomere length as a target for therapy. Townsley et al. studied the use of synthetic danazol, the sex hormone, to attenuate the accelerated telomere attrition since, in tissue culture and animal models, sex hormones regulate expression of the telomerase gene. The results showed that almost all the patients  had a gain in telomere length at 24 months as compared with baseline . A recenTherefore, this is a field with possibilities for new, more personalized therapeutic approaches in the era of precision medicine.The mean age-adjusted telomere length in IPF patients was significantly shorter than that in age-matched controls. In addition, telomeric length has been found to be an independent risk factor in several studies ,61, beinIt has also been shown to be associated with a worse post-transplant prognosis and a greater number of associated complications, both due to the use of immunosuppressive medication, which, as previously mentioned, is related to adverse effects in these patients, as well as increased mortality related to shorter telomeres. It is suggested that poorer transplant survival may be related to higher rates of chronic lung allograft dysfunction and a shorter time to its onset  (p. 6).On the other hand, although age in itself is a poor prognostic factor for IPF in general , the opposite has been seen in patients with telomeric shortening. The younger they are at diagnosis, the worse their evolution will be. This can be explained by the fact that the earlier the fibrotic disease presents, the faster the lung aging process develops  (p. 4).Telomere length is a biomarker capable of preventing a worse prognosis and therefore, can help to stratify risk and decide on a referral for transplantation. Therefore, its measurement is recommended as part of the pretransplant workup of patients with IPF.On the other hand, telomeric shortening has also been shown to be associated with increased risk of infection and increased mortality when infections occur ,63. HencIn IPF, follow-up is performed with pulmonary function tests, especially forced vital capacity (FVC). FVC decline is predictive of mortality in patients with IPF. However, there is currently no useful biomarker in clinical practice to predict this decline. Consequently, as telomeric shortening predicts a susceptibility to develop progressive fibrosing disease, it could be used together with other biomarkers as a prognostic factor for survival, to help us know the patient’s evolution and decide on possible therapeutic options.Several studies have shown that telomeric shortening does not occur only in IPF but has also been related to other types of interstitial lung diseases, such as unclassifiable diseases, hypersensitivity pneumonitis, and pleuroparenchymal fibroelastosis, among others .In these diseases, telomeres are shorter compared to controls; however, there are differences between each of the entities. Above all, a greater shortening has been seen in the case of IPF, implying a genetic factor, innate rather than acquired as in the other entities, likely acquired through increased oxidative stress or inflammatory responses  (p. 2).Moreover, telomere-related mutations have been identified in patients with non-IPF fibrosis. However, the one consistent phenotype seen across all genetic mutations is progressive deterioration. So, although clinical diagnosis is important for therapeutic and prognostic decisions, the identification of telomeric shortening or mutation in its related genes predicts a similar pathogenic mechanism, which manifests in a progressive fibrosing phenotype and confers a worse prognosis.IPF is an aging-related interstitial lung disease of unknown etiology with a progressive evolution and an often fatal outcome. Telomere pathology has been linked to epithelial cell senescence, which has been identified in the lining fibroblast foci of IPF lung.Pulmonary fibrosis is the most frequent manifestation of telomeropathies. Several studies have shown that telomeric shortening is a risk factor for the disease, regardless of whether or not mutations in specific genes are identified.Age- and sex-adjusted telomere length is shorter in IPF patients than in the general population, in both the familial and sporadic forms of pulmonary fibrosis, and regardless of having mutations in related genes. Moreover, telomeric shortening is more marked in patients who have a worse prognosis.The clinical course of IPF is variable, and no reliable prognostic biomarkers are currently available. Therefore, the study of telomeric shortening in IPF provides prognostic data, functions as a biomarker, helps us prevent complications related to certain medications, and could help us in the risk stratification prior to transplantation.It is important to know the characteristic features of telomeropathies in order to identify those patients susceptible to telomere measurement and/or genetic study, as for the time being, these tests are not yet widely available and are expensive.Modulation of telomerase expression and telomere length may be a therapeutic tool for the treatment of these disorders. Future research should focus on the molecular mechanism underlying the shortening of telomere length in IPF.


Contact resistance between electrically connected parts of electronic elements can negatively affect their resulting properties and parameters. The contact resistance is influenced by the physicochemical properties of the connected elements and, in most cases, the lowest possible value is required. The issue of contact resistance is also addressed in connection with the increasingly frequently used carbon allotropes. This work aimed to determine the factors that influence contact resistance between graphene prepared by chemical vapour deposition and pre-patterned Cu and Au electrodes onto which graphene is subsequently transferred. It was found that electrode surface treatment methods affect the resistance between Cu and graphene, where contact resistance varied greatly, with an average of 1.25 ± 1.54 kΩ, whereas for the Au electrodes, the deposition techniques did not influence the resulting contact resistance, which decreased by almost two orders of magnitude compared with the Cu electrodes, to 0.03 ± 0.01 kΩ. RC) between the metallic contact and the graphene can substantially affect the performance of the transistor realised in this way [RC significantly affects the high-frequency characteristics of the transistors [RC, the sensor’s sensitivity is lost. The arrangement of these sensors is usually reversed compared with that of the GFET. The electrode structures are first created using photolithography, and then the active layer of graphene is applied on top. Graphene-sensitive layers are used to realise sensors intended for a wide range of applications, such as sensors for monitoring environmental parameters and biosensors [Carbon allotropes, such as graphene or carbon nanotubes, are advantageously employed in many applications , such asthis way . It was nsistors . A numbensistors , mild oxnsistors , selectinsistors , or postnsistors . Howeverosensors ,13,14,15osensors , where tosensors  and temposensors . The higosensors  or epitaosensors , but theosensors , which aosensors . As an iosensors . The CVDosensors .RC values below 100 Ωµm [The transfer length method (TLM) in various modifications is the most frequently used technique to evaluate the contact resistance between graphene and the electrode structure ,9,24. ThThe aim of this work was thus to verify whether it was possible to achieve reasonably similar values of contact resistance for copper as for gold electrodes when pre-patterned before the deposition of single-layer (1L) CVD graphene. We also investigated how various surface treatment protocols of such an oxidising electrode system would affect the contact resistance and, finally, whether the contact resistance between gold and graphene could be further improved by its structural modifications, as suggested previously for better-defined combinations of higher quality graphene and metal electrodes deposited on top ,9,10.An electrode structure was designed for the experimental part, where the gaps between individual electrodes were set to 25, 40, 60, 80, 100, 200, and 300 µm .2 layer. The sputtering deposition machine Balzers BAS 450 PM  was used to deposit the Cu or Au/NiCr conductive layer. The thickness of the NiCr layer was 80 nm and the Au or Cu layer thickness was 150 nm. The exposition device Karl Suss MA 56  was used for the photolithography process, where the deposition of the resist ma-P1225 by Microresist Technology  was carried out by spin coating. The thickness of the coated resist was 2 µm. As a carrier substrate, the 3-inch boron-doped silicon wafer from Nanografi  was used, with a thickness of 380 µm, a resistivity of 1–10 Ωcm, and a SiO2 thickness of 300 nm. At the end of the electrode structure realisation, the wet etching process was used for 30 s in the solution of HNO3, Ce(SO4)2, and H2O. The actual gaps between individual electrodes were measured using an Olympus confocal scanning microscope, LEXT OLS5000 . The measurement of the gap between the electrodes was carried out at three different places for a given gap, and the arithmetic mean was used for the precise evaluation of RC. The standard deviation of the measurement was ±0.96 µm for a gap of 25 µm and ±1.0 µm for a gap of 300 µm, which shows a negligible influence of the gap dimension heterogeneity on the evaluation of the resistance measurement results.The metal electrode structure based on a Cu or Au/NiCr layer was realised using photolithography on silicon carrier substrates (Si wafers) with a SiO3 , or electrochemical polishing using H3PO4 . The mechanical cleaning (MC) of the electrodes consisted of fine grinding and polishing with the use of a rubber made from synthetic polymers (bis(2-ethylhexyl)hexahydrophthalate), during which oxides were removed from the surface of the electrodes. Chemical cleaning (CC) was performed using nitric acid in three different concentrations . Electrochemical polishing (ECP) of the electrode structure was carried out in a phosphoric acid solution (50 wt.%) upon applying 1 V vs. Ag/AgCl for 5 or 8 s [Before the actual deposition of the graphene, various methods of Cu electrode surface treatment were applied . This in5 or 8 s . The ele2 atmosphere and annealed for 40 min. Afterward, the foil was exposed to a mixture of CH4 (1 sccm) and H2 (50 sccm) for 30 min. Additional annealing in H2 for 5 min was performed after exposure to the methane/hydrogen mixture. Total pressure during the entire CVD process was maintained at 350 mTorr. Finally, the substrate was cooled down under H2. The 1L graphene was transferred onto electrode structures using the nitrocellulose-assisted method [2  using an in-built flow controller, after which the pressure was stabilised and the plasma was turned on at 35 W or 52.5 W RF generator for a specific treatment time of 10, 12, or 20 s . Ions extracted from an ion source with a 20 kV voltage were analysed with a 90° magnet and accelerated with an 80 kV voltage. The samples were kept at room temperature during irradiation and the beam current was 50 nAcm−2. The beam current and total fluence were measured using a shoot-through Faraday cup with an aperture of 1.4 cm in diameter and a Faraday cup area of 1.5 cm2. For electron suppression, the Faraday cup had suppression electrodes at −500 V in front of and behind the Faraday cup. The defects in Ar+-bombarded samples were created only within the graphene in contact with the Au electrodes using direct-write photolithography patterning  with an AZ® ECI 3007 chemical resist  which, after illumination, was developed with an AZ® Developer .Graphene was grown by CVD in accordance with a previously established protocol ,27,28. Bd method . Lists o20 s see . They wecm−2 see . The samRT) is a combination of the contact resistance of graphene with the two neighbouring contacts (RC), the resistance of the graphene layer (RG) between the contacts, and the intrinsic resistance of the metallic layer (Rm):The TLM was used to determine the contact resistance values between graphene and metal electrodes. This technique consists of creating metal electrodes with different gaps on which the graphene layer is deposited. The resistance value is then measured between the individual contacts. The measured total resistance value . From the slope of the linear fit, the sheet resistance (RS) of graphene can be obtained, because RG = RS(L/W), where L is the distance between the electrodes and W is the contact width. However, in this work, RS was determined from the particular slope of measurement between contacts spaced at 100 and 200 µm, where the total resistance is dominated by the RG and less so by the RC.Because RC values. The measurement of contact resistance was carried out in DC mode concerning the TLM evaluation. Contact with individual samples was made using a homemade contact needle array prepared for the used electrode structure, for which the spring probes S1 Series from Smiths Interconnect  were used. Samples 14 to 16 were measured in darkness using a Keithley 4200A-SCS parameter analyser  in a two-point arrangement with gold probes , and RT values were extracted from I-V characteristics. The electrical resistance was gradually measured between the individual electrodes . The effect of light on the contact resistance values for samples that were measured under laboratory lighting but kept in the dark, and for samples measured directly in the dark, was not observed. The measured values of the electrical resistance were plotted in a graph as a function of the gap between the individual electrodes.After the graphene was deposited on the electrodes, the resistance between the individual electrodes was gradually measured. The measurement of electrical resistance was carried out at room temperature and pressure, under standard illumination, using the four-point test method via the multimeter system Keithley DAQ6510  to eliminate the possible influence of supply wire resistance. The measurement accuracy of the resistance for the Keithley equipment was 0.01% of the reading and 0.001% of the range. This high measurement accuracy cannot cause a significant deviation in the measured total resistance values and, therefore, neither in the resulting −1. The spectrometer was calibrated using the F1g mode of Si at 520.2 cm−1. The defect density, nD, and the average distance between defects, LD, in the 1L graphene were calculated using Equations (2) and (3) by using the intensity (amplitude) ratio of the D and G Raman bands (ID/IG) and the laser excitation wavelength (Lλ) [Raman spectra of the transferred graphene (both pristine and with post-transfer defects) were measured using either a 532 nm laser excitation wavelength  or a 633 nm laser excitation wavelength  under a 100× objective (0.85 N.A.). The laser power was 1 mW under the objective. A grating of 600 lines/mm was used, giving a pixel-to-pixel resolution of ~1.8 cmy-axis determines twice the RC value between the electrode structure and the graphene layer.The core result of all individual experiments is the dependence of the resistance between every two neighbouring electrodes on the distance between these electrodes. The measured values for each sample were subsequently extrapolated by a least-squares linear fit, where the intersection with the In a copper electrode system, copper oxidation has a significant effect on contact resistance. For this reason, it is necessary to remove the oxide layer before graphene deposition. Samples with an applied graphene layer without any surface treatment of the copper electrodes were not realised and measured because the oxidised surface of the copper electrodes will significantly increase the contact resistance, thereby preventing any meaningful electrical measurement. Different methods of cleaning the copper electrode structures were compared to evaluate the efficacy of such treatments on the contact resistance between the copper electrodes and the graphene layer that was transferred on top of them immediately after the cleaning procedure. RC values for different methods of the surface treatment of copper electrodes. The lowest RC values were achieved for the MC and CC, reaching hundreds of ohms on average. For the ECP, the contact resistance was the highest, in the order of kΩ units. These higher RC values are probably due to the residual oxide layer that was not removed during this surface treatment. As can be seen in RC value was achieved for the longest electropolishing time—although it was still higher than for most of the other treatments. Hence, in contrast to the other treatment types, it would be necessary to set the conditions of the cleaning process according to the thickness of the oxide, making this method generally less viable for standardised utilisation. Concerning the other methods, the RC values of hundreds of ohms indicate that, despite the removal of most of the copper oxide, no good electrical contact with graphene was realised. Such high RC values are probably caused by a thin and heterogeneous layer of copper oxide forming instantaneously on the cleaned electrodes during the graphene transfer procedure. Furthermore, the oxide layer can grow even under the graphene layer due to the defects present in its structure, and the surface of the electrodes is thus still partially accessible to air. The air can also penetrate under the graphene layer from its edges. As shown previously, the graphene layer on the copper electrodes worsens the oxidation [RC values during repeated measurements after 14 days of aging when the samples were left in an ambient environment . xidation , therebyRC values between these two groups  of the graphene layer was also determined for the samples. As expected [RS can be observed for the plasma-treated samples. In conjunction with the Raman spectroscopy measurements, shown in −1 increases with the increasing treatment times and powers for the O2 plasma and with the total dose for Ar+ ion bombardment. At the same time, the intensity of the 2D band (at ~2700 cm−1) decreases, also corresponding with an increasing lattice disorder [ID/IG), and the ranges of defect densities (nD) and distances (LD) calculated using Equations (2) and (3), are summarised in ID/IG varies from 0.11 to 3.94, with the corresponding nD from the lower limit to the upper limit. We note that due to different laser excitation wavelengths used for the Raman measurement, only the ranges of nD and LD values should be taken as quantitative, not the average value, due to the approximation in Equations (2) and (3) [−1. The intensity ratio of the D and D’ bands (ID/ID’) is related to the defect type [2 plasma and 14 for the Ar+ bombardment), the ID/ID’ ratios are ~20.6 and 5.5, respectively. These ratios indicate that most of the defects are sp3-like for the plasma-treated samples and vacancy- or boundary-like for Ar+ irradiated samples. Hence, regardless of the majority defect type, the contact resistance is still dominated by the graphene deposition on top of the electrodes. A visual comparison of the effect of the defect density is shown in RC is plotted against nD. The squares with colours corresponding to the spectra in RC values for the non-modified samples, confirming that in our graphene–electrode system, defects with areal density up to ~6 × 1011 cm−2 do not influence contact resistance more than the differences caused by imperfect control of the graphene transfer process. We also note that regardless of the defect presence, the RC values are stable over time, in contrast to the case of Cu electrodes  . Additioect type ,41. In oodes cf. .RC reached an average value of 1.25 ± 1.54 kΩ for Cu and only 0.03 ± 0.01 kΩ for Au, with the same electrode dimensions. In previous studies, where the metal electrodes were fabricated on top of the graphene samples, several reasons for the differences in RC between graphene and various metals were suggested. The work function difference between graphene and the metal can cause differences in the charge doping of graphene, thereby changing the density of its states and, consequently, sheet resistance [RC with graphene. Instead, they suggested that chemical cleaning and the microstructure of the deposited metal are the key factors influencing contact quality. In our case, however, the large difference between RC values for Cu and Au, the heterogeneity between individual treatment types and samples on Cu electrodes, and the obvious worsening of RC over time for Cu, all point to the major influence of the copper oxide removal and subsequent continuous growth. The Cu electrodes, in spite of the lower cost, are thus not favourable for the system in which they are pre-patterned under graphene.As can be seen from the results presented in the above sections, the contact resistance between CVD graphene and a pre-patterned electrode beneath it is highly dependent on the particular contact metal. The sistance . The worsistance . Therefosistance . In contsistance  reported11 cm−2, regardless of their type and origin. The achieved results can thus be the basis for the design of sensor elements based on graphene structures, as it is clear from these results which methods of modifying the electrode system and methods of graphene transfer have the greatest effect on the contact resistance and, thus, the resulting sensitivity of the sensor. Fabrication of pre-patterned electrodes can also be potentially utilised in other applications, where the graphene’s top surface is sensitive to post-processing, such as when manipulating its electrochemical or optical properties by molecules or nanoparticles.We investigated the quality of electrical contacts between 1L CVD graphene and Au and Cu electrodes pre-patterned on the substrate before the transfer of graphene on top. It is clear from the obtained results that in the case of the graphene layer on the Cu electrode system, the type of cleaning of the electrode structure before the graphene transfer strongly affects the resulting contact resistance. Nevertheless, the lowest achieved contact resistance values were still in the order of hundreds of ohms. Such high contact resistance values can dampen any desired changes in the resistance of the graphene layer when it is utilised as the active layer in chemoresistive sensing applications. In the case of Au electrode systems, contact resistance in the order of tens of ohms was achieved without the need to clean the electrode system surface before the transfer. However, the contact resistance was not further improved in the Au–graphene system by modification of the graphene structure by defects with densities reaching 6 × 10


This study presents an easy and quick method for the synthesis of graphene from graphite in a set of solvents, including n-Hexadecane (n-Hexa), dimethylsulfoxide (DMSO), sodium hydroxide (NaOH), 1-octanol (OCTA), perchloric acid (PA), N,N-Dimethylformamide (DMF), ethylene glycol (EG), and ethylene diamine (ED), via microwave (MW) energy. The properties of final products were determined by X-ray diffraction spectroscopy (XRD), Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible (UV-Vis) spectroscopy, and the four-point probe technique. The XRD spectra of most of the MW-assisted graphene products showed peaks at 2θ = 26.5° and 54°. Layer numbers extend from 2 and 25, and the leading comes about were gotten by having two-layered products, named as graphene synthesized in dimethylsulfoxide (G-DMSO), graphene synthesized in ethylene glycol (G-EG), and graphene synthesized in 1-octanol (G-OCTA). G-DMF has the highest electrical conductivity with 22 S/m. The electrical conductivity is higher when the dipole moment of the used solvent is between 2 and 4 Debye (D). The FTIR spectra of most of the MW-assisted graphene products are in line with commercial graphene (CG). The UV-Vis spectra of all MW-assisted graphene products showed a peak at 223 nm referring to characteristic sp2 C=C bonds and 273 nm relating to the n → π * transition of C-O bonds. Graphene, a monolayer form of graphite, has gathered significant interest in the past few years due to its outstanding properties, such as high electrical conductivity, extraordinary mechanical strength, and large specific surface area . By meanLiquid phase exfoliation (LPE) of graphite presents more economical, easier and a scalable alternative for the graphene production in comparison to Hummers’ method ,16,17. L−2 which is an optimum range for the MW-assisted exfoliation of graphite. Moreover, Pal et al. found that expanded graphite content increased the dielectric properties of the composite with epoxy. The dielectric constant and dielectric loss factor of expanded graphite produced by chemical oxidation were decreased when the microwave treatment was applied to these composites [The solvents having near zero surface tension can penetrate the gap between the graphite layers. This results in an abrupt expansion, generating a force that facilitates exfoliation of graphene in a liquid . Microwamposites . In this study, a simple and rapid method was presented for graphene synthesis and the one-pot synthesis of graphene from graphite through a MW-assisted solvothermal technique was conducted by using various types of low-cost solvents, such as n-Hexadecane (n-Hexa), dimethyl sulfoxide (DMSO), sodium hydroxide (NaOH), 1-octanol (OCTA), perchloric acid (PA), ethylene glycol (EG), and ethylene diamine (ED). Then, the exfoliation capabilities of aforementioned solvents were compared by examining the properties of final products via X-ray diffraction  analysis, the four-point probe technique, Fourier transform infrared spectroscopy , ultraviolet-visible  spectroscopy, Raman spectroscopy , atomic force microscopy  analysis, Brunnauer–Emmett–Teller  analysis, and scanning electron microscopy  analysis.The pristine graphite powder, grade 3061, was purchased from Asbury Graphite Mills, Inc., New Jersey and labelled as commercial graphite (CGr). Commercial graphene (CG) was acquired from Ningbo Yuanshi New Material Technology Co., Ltd., Ningbo, China and used as the reference. N-Hexadecane , dimethyl sulfoxide , sodium hydroxide , 1-octanol , perchloric acid , ethylene glycol , and ethylene diamine  were of analytical grade.®, Berlin, Germany) under 200 W, 35 kHz, mode 5, and 50% power for 10 min. Afterwards, MW irradiation was applied to the reaction mixture in a 80 mL volume of Teflon vent-and-reseal vessel under the conditions of 180 °C, 150 W for 30 min by a multimode Start-S model  MW furnace shown in It is worth to be mentioning that the graphene synthesis protocol that is presented in this study is a totally novel method. Firstly, the graphite was dispersed in various solvents at a concentration of 100 mg/mL. The mixture was then sonicated just prior to MW to prevent agglomeration via HD 2200 SonoPuls Homogenizer  spectroscopy. For UV-vis analysis, the dried samples were dispersed in distilled water by agitating via a magnetic stirrer. Afterwards, some of the dispersion was taken into the 10 × 10 mm vial and spectra were recorded at a range of 200–700 nm. The spectrum has an operation range of 200–700 nm. Brunnauer–Emmett–Teller (BET) surface area values of the products were measured by N2 physisorption at 77K using a Quantachrome Autosorb Automated Gas Sorption System. The samples were degassed under vacuum at 380 °C for 20 h prior to measurement. The thickness of the samples was determined via a Universal Scanning Probe Microscope (USPM) model Atomic Force Microscopy (AFM) that was built in 250× video microscope, easy change cantilever, a 40 µm × 40 µm × 4 µm scan tube assembly and Acoustic/Vibration Isolation Chamber (AVIC). The microstructures of the synthesized graphene were determined using a scanning electron microscope (FEI Philips XL30 SFEG SEM).The graphene samples were characterized by analyzing the thickness, the layer number, and the electrical conductivity of the final products. In order to determine the layer number, X-ray diffraction (XRD) analysis was performed via Rigaku D-Max 2200 Series equipped with Cu-Kα radiation (λ = 1.54 Å) at a scanning rate of 3° per minute. The tube voltage and the current were 40 kV and 40 mA, respectively. The intensity was determined over a 2θ° angular range of 2–90°. Electrical conductivities of synthesized products were measured using a Keithley 2400 Sourcemeter following the manufacturer’s instructions. Firstly, a graphene sample was placed in a cylindrical copper container equipped with a copper cap, thereafter, compressed by a hydraulic press at 50 bars (5 MPa) for 30 min. The electrical resistivities of final products were evaluated by the 4-point probe method. Graphene powder samples were compressed in a copper container with the help of a joiner’s clamp during the electrical conductivity measurements. The conductivity, σ, was then estimated as in Equation (1) shown at below:In this study, graphite was converted to graphene using various solvents with the help of MW energy. The MW-assisted LPE technique was applied to a set of graphite-solvent dispersions in order to exfoliate the graphite to graphene. It is possible to measure the affinity of solvent molecules to graphene layers by physical parameters such as dipole moment (μ), dielectric constant (ε), and surface tension (σ). Moreover, the effect of the surface tension, dipole moment, and dielectric constant of the solvents on layer number and electrical conductivity results were examined in order to increase the product quality. The solvents having larger dipole moment, dielectric constant and surface tensions show better solvent properties than those having poor solvent characteristics for a few exceptions . The cheIn the literature, it is understood that N-methyl-pyrrolidone (NMP) and dimethylformamide (DMF) provide better efficiency in regard to the exfoliation of graphite ,28,29. TThe thickness of graphene products and CGr was calculated by substituting the results of XRD analyses into Scherrer’s equation, which is denoted as Equation (2) shown below: ng angle ,33. The = 26.69° ,34. As s= 26.69° ,22,35,36Moreover, XRD analyses of final products were performed in order to prove the graphene crystal structure. The XRD spectra of the samples were presented in D-spacing values of graphene products were estimated by using Bragg’s equation (Equation (4)), which is shown at below:According to the The relation between the surface tension and layer number is presented in The relation between layer numbers and dielectric constant is given in The relation between electrical conductivity and layer number is given in The structure of the graphene products was also evaluated by FTIR analyses of CG, G-OCTA, G-DMSO, G-NaOH, G-n-Hexa, G-EG, G-ED, and G-PA, as presented in −1 due to the remaining carbonyl groups after the sonication and MW irradiation process [−1, corresponding to C-O-C bonds, is from remaining epoxides and other ether groups [−1 indicates the residual of solvent coming from filtration process [2 C=C bonds was found at 1618 cm−1 which have been assigned to skeletal vibrations of graphitic domains [−1 is characteristic of -COOH (-C=O in carboxylic acid), which is known to appear after purification steps following reaction and exposure to air [−1 correspond to the asymmetric stretch mode of atmospheric carbon dioxide, which is routinely observed in the background scan on an FTIR measurement, while the peak is caused by the absorption of CO2 from the air [−1 was due to O-H stretching vibrations [The FT-IR spectra of G-PA indicates graphene oxide (GO) formation and reveals the presence of various functional groups, including C-O-C and C-O stretching vibrations observed as a very low intensity band at 1083 cm process . The pear groups . The pea process . The sp2 domains ,46. The e to air . The ban the air . The brobrations . The FTIThe UV-vis spectroscopy is one of the most efficient techniques showing the presence of the bonds and the functional groups in the graphene structure. The UV-vis spectra of commercial product (CG) and the MW assisted synthesized graphene products are presented in The absorbance peak at 223 nm is related to the π → π * electron transition of C-C ring. The absorbance peak at 273 nm is related to n → π * transition of C-O bonds . PreviouIn order to approve the graphene structure, Raman spectroscopy was performed using final graphene samples. The Raman spectra consisting of CG, G-PA, G-EG, G-DMSO, and G-OCTA are given in −1, G band at 1566 cm−1, and 2D band at 2689 cm−1. The average value of IG/I2D for all samples is 1.72. This indicates that all samples have single layer. A noise peak around 2315 cm−1, which is attributed to C-O structure, was seen for all the samples [−1 is characteristic to sp2 C=C bonds. In the Raman graph, the D band, also known as the disorder band, occurs due to lattice motion away from the middle of the Brillouin zone and its presence between 1270 and 1450 cm−1. Thus, this means that G-OCTA does not demonstrate a good graphene structure.The Raman spectra of all graphene products showed a D band at 1350 cm samples . These rBrunnauer–Emmett–Teller (BET) surface area values of products labeled as CG, G-OCTA, G-EG, G-DMSO, G-PA, and G-n-Hexa are given in 2/g for CG, G-OCTA, G-EG, G-DMSO, G-PA, and G-n-Hexa, respectively. It can be concluded that the graphene products named as G-EG, G-DMSO, and G-PA having the most promising layer number results also have large BET surface area values. CG has a smaller BET surface area than MW-assisted synthesized graphene products. Kamelduski et al. proposed that graphene with a high surface area can also be used in energy storage devices, especially Zn–air batteries [According to atteries . The thickness and layer numbers were also estimated by AFM analyses. The AFM results of final graphene products are given in The thickness of G-DMSO, G-EG, G-OCTA, G-PA, and CG is determined as 2056 nm, 418.85 nm, 1435 nm, 587 nm, and 9.87 nm and the layer numbers of G-DMSO, G-EG, G-OCTA, and CG are calculated as 6137.313, 1250.298, 4283.58, 1752.23, and 29.02, respectively. When comparing with CG, produced graphene samples showed multilayered structures. While comparing the layer numbers obtained by AFM with the layer number values that were calculated by XRD and presented in SEM results show that the obtained graphene products have a wrinkled and agglomerated structure. As seen in −1, G band at 1566 cm−1, and 2D band at 2689 cm−1 which are characteristic to graphene. AFM analyses demonstrated the multilayered structure of some of the samples. Moreover, SEM results proved that the obtained products have a wrinkled and agglomerated structure as is usually encountered in the graphene images.This study presents a quick and easy way for synthesizing graphene from graphite via microwave irradiation. This method depends on LPE of graphite in different solvents whose surface tensions are in the range of 25–75 mN/m. LPE is a top-down method which eliminates the disadvantages related to the traditional Hummers’ method, including using heavy and toxic chemicals and successive production steps. After the reaction and purification steps, obtained carbon products were characterized by using different characterization methods, such as XRD, four-point probe electrical conductivity measurement, FT-IR analysis, UV-vis spectroscopy, Raman spectroscopy, AFM spectroscopy, and SEM analysis techniques. The XRD spectra of the samples showed two peaks at 2θ = 26.5° and 54° which are characteristic of graphene. According to XRD results, obtained samples have 2–25 layers. Most of the products are few layered. Among these products, those with lowest graphene layers were determined as 2, 2, 5, and 2 for G-DMSO, G-OCTA, G-PA, and G-EG, respectively. When the dielectric constant of the used solvent increases, the electrical conductivity values increase too. The FTIR spectra of most of the graphene products showed no sharp peaks and were compatible with the FTIR spectrum of CG. The UV-Vis spectra of all the graphene products display three peaks 223, 240, and 273 nm which refer to the π → π * electron transition to C-C ring, red-shifting of 223 nm domain, and n → π * transition of the C-O bond. The Raman spectra of all products showed peaks at D band at 1350 cm


Predicting long-term student achievement is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for online formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, online formative assessment often involves open-ended questions that students answer in writing. These types of questions in online formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low socioeconomic status (SES) schools. Using regressors obtained from linguistic features of the answers and an automatic incoherent response classifier, we built a linear model that predicts the score on an end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students’ open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on online formative assessments. One of the most recommended teaching and learning strategies is formative assessment . These aIn this paper, we use a database of questions and answers taken from ConectaIdeas . This isIn principle, open-ended questions allow teachers to visualize their students’ reasoning. Additionally, promoting written answers to open-ended questions helps not only to reason about calculations but also to acquire written communication skills. This is particularly true if the question asks for explanations. These are questions that require more effort from students, but which are posed much less frequently than closed questions. Moreover, these are teacher-adapted or teacher-designed questions, thus leading to a wide range of question types. It is therefore more challenging to estimate their long-term learning than when working with questions taken from a carefully crafted and small list of closed questions. In this paper, we examine the responses of fourth-grade students from at-risk schools. The answers are very short. The average number of words in the responses is eight to nine words. This average increases as the school year progresses . On the Research question: To what extent do students’ short, written answers to teacher-designed, open-ended questions in weekly online formative tests help improve predictions of performance on end-of-the-year national multiple-choice standardized assessments?In this section, we examine related papers that investigate the possibility of making predictions of long-term learning and, on the other hand, the effect of answering open-ended mathematics questions.From a sample of 23,000 learners in Grades 6, 7, and 8 over three academic years, p-values less than 0.05) when testing is immediate but not after there is a delay.These papers show that the score at the end-of-the-year tests can be predicted with a reasonable degree of error and that written answers to open-ended questions help the learning process. However, in the prediction literature, there are not yet regressors related to the written answers to open questions .Our data come from a virtual platform called ConectaIdeas. This platform has a series of mathematics exercises for elementary school students. Each student answers two types of online questions during weekly sessions. The questions are created by teachers and can be either closed-ended  or open-ended . Throughout the school year, students answer the 36 closed-ended questions and one or two open-ended questions during each session. The sessions are 90 min long, with two sessions held per week. Unlike the answers to closed-ended questions, the data for the answers to the open-ended questions are unstructured and provided in the form of a written text. This is a particularly challenging feature.Below, we outline the materials and methods used to predicting the scores on an end-of-year national standardized test.We focused on using data gathered from the ConectaIdeas platform during 2017 from both open-ended and closed-ended online exercises. Fourth grade students used this platform to exercise mathematics content. Additionally, in order to measure progress in terms of performance when using the platform, 24 schools participated in an RCT study. In this study, only one of the two classes in each school worked on the platform (treatment group), while the other class did not have any access to the platform (control group). We will only focus on the responses given by students in the treatment group, since they are the only ones who answered both open- and closed-ended questions during the year.Now, for simplicity, we only consider two important annual tests: one at the beginning of the year and the other at the end of the year. At the beginning of the year, a test was applied to measure the performance of students prior to participating in the study. This test is called the pre-test. The second test, called SIMCE, is a national standardized test that is conducted every year by an educational quality agency. We consider the pre-test per student as an estimator to predict the score on the end-of-year national standardized test (SIMCE). It is worth noting that at a national level, this standardized test has a mean of 260.95 and a standard deviation of 47.8, both of which are important reference values .Additionally, we include the average SIMCE score obtained by the school in 2016 as another estimator. Note that this estimator is at the school level and not per student. In addition, we automatically label responses to open-ended questions with two prediction models. The first is a question type detector, which consists of receiving a question and responding with one of six question types. These are: (1) Calculate without explaining, (2) Calculate with explaining, (3) Choice and/or affirmation, (4) Compare quantities, (5) Procedure and content knowledge and (0) Others. The second is a coherent answer detector, which receives both the answer and the question in order to detect whether or not the answer is incoherent. Answers that range from noisy text  to nonsensical responses  are considered to be incoherent answers.In the ConectaIdea platform, answers to open-ended questions do not receive a score according to the correctness of the answer to the open-ended question. However, for this study, years later, teachers labeled answers received in 2019 according to coherence, indicating whether the answer is incoherent or not. With this information, we fit a model capable of automatically predicting the coherence of written answers to open-ended questions. Then, we used that model to label the student answers received in 2017. In addition, coherence does not imply correctness, but the reverse is true. This information is used for two reasons: (1) Some of the incoherent answers  are not appropriate for extracting linguistic features such as part-of-speech and dependency-tags. In addition, separating coherent answers from incoherent ones allows us to make sure that the answers are appropriate to the question asked and (2) incoherent answers also reveal a student’s animic, attitudinal, and not necessarily cognitive state. If the student responds with a scribble or something disjointed, it reveals a disrespectful, negative attitude or protest behavior. If he or she responds with something completely inappropriate, it is very different from making a calculation error or misexplaining a result by making conceptual errors. As Below, we will perform a general analysis of the data for the prediction of 2017 SIMCE scores. In our case, we are interested in all those students on the platform who have answered at least one open-ended question (these are the written answers). In summary, only 58.29% of the students out of a universe of 796 students from across 24 schools have answered this type of question at least once. This corresponds to 464 students. Additionally, 44.59% of the 796 students are female. It can also be observed that neither the sex of the student nor the number of students with answers to open-ended questions are stable at the school level, since there are schools with more girls than boys and others with 78.04% of students with responses to open-ended questions.https://www.junaeb.cl/ive, accessed on 16 March 2022). The weighted average of IVE (according to the number of students per school) for our data is 90.2%. The average of the IVE in Chile is 80.5% and the standard deviation is 17.2%. Thus, the IVE of these 24 schools is 0.56 standard deviations above the national mean. On the other hand, the average SIMCE score of the 24 schools in 2017 is 0.381 standard deviations below the SIMCE national average of that year. Despite this, there are students in some schools that stand out with 2.45 standard deviations above the national average.Now, we filter by all students with at least one response to an open-ended question. When we reduce the sample to this universe of students, we obtain a more even distribution between male and female students . It should be added that the 24 schools we studied correspond in general to at-risk schools, with a high percentage of vulnerability. For this, we use the index of vulnerability (IVE). This is an index that the Chilean Ministry of Education officially calculates for each school .Dynamic. Unlike the previous ones, these variables can vary during the year, e.g., number of online exercises answered on the first attempt. In this context, these variables are usually called Process variables . The objective is to transform all this information into a few relevant variables. For this, attributes are extracted from each of them independently . In this way, each student’s answer is encoded with a vector representation where each component of the vector is an interpretable variable. As each student answers more than one question, a stack of vectorized answers will be obtained . However, since each student answers a different number of questions per year, if we concatenate the rows of this matrix, we will obtain vectors of different lengths for each student. This is a problem when considering these as regressors for the linear model. To solve this, we will reduce the matrix to a single vector with the same number of columns, where each component condenses only the information of that column on individual vectors of the matrix . Finally, as we wish to keep those components with the best predictions on the linear model, dimensionality reduction will be applied .Simple indicators. These are indicators that allow data to be aggregated: in our case, to aggregate the answers to open-ended questions. One indicator is the number, e.g., number of incoherent answers. Another one is the proportions, e.g., the proportion of coherent answers. In addition, there are double aggregate indicators, e.g., number of coherent answers to calculate with explaining questions.Traditional/Semantic/Contextual features. Different features can be extracted from any given answer. These are Traditional, Semantic and Contextual features. Traditional attributes are those that capture simple properties of the sequence of letters in words and the sequence of words in sentences, e.g., number of vowels and number of words. Semantic attributes, on the other hand, correspond to properties that have a conceptual charge in words, e.g., number of well-spelled words or curse words or emoticons. Meanwhile, Contextual attributes are those that depend on the environment of the sentence; in the case of answers, their context is the question, e.g., the number of similar words between the answer and its question. Given all the answers that are given by a student, the way in which these features are distributed is important, e.g., Average of number of words in coherent answers. These features can also be aggregated by question type, e.g., Standard deviation of number of numbers in answers to calculate without explaining questions.Linguistic features. Unlike the previous features, this time, we are interested in using linguistic knowledge to capture useful information from the answers. The most abstract features of an answer are (shape) the shape of the tokens and  indicator of whether the token is alphabetic. An example of this is the following:Some words are noted for their abundance; these are called stop-words, e.g., https://explosion.ai/, accessed on 16 March 2022).We will now consider two more attributes, but this time, they depend on the other words and need to be detected automatically. These are part-of-speech tagging (POS tags) and dependencies (dep), both of which are available in the Spanish version of the Spacy library . An example of a regressor using POS tags is the following: Median number of verbs (tag/VERB) in coherent answers.POS tags are detected for each token in a response and can be: (PRON) Pronoun, (ADJ) Adjective, (VERB) Verb, and (NUM) Numeral, among others (Available in candies from the response There are 25 candies then 25 will take the role of the root (dep/ROOT) of the sentence that had candies; Likewise, if we add candies then 25 obtains its characteristic of being a number with units, which is a dependency called a Numeric modifier (dep/nummod). An example of a regressor using dependency tags is the following: Average of number of dep/nummod in coherent answers. Other interesting dependency tags include: (obj) object, (nsubj) nominal subject, and (nmod) nominal modifier, among others .Dependency tags correspond to syntactic dependencies between the tokens of the answer; e.g., if we remove In the following, we will detail the types of regressors based on written responses that we consider for the Open-ended model:Filters. The simplest way to construct a regressor based on the answers is to consider all of them. Another way is to use a subset of the answers, e.g., only incoherent answers. This way of filtering is based on coherence. In addition, the type of question associated with the answer is automatically detected so that it can be grouped according to question type, e.g., only answers to questions of type Others. Likewise, both filters can be applied, i.e., both according to coherence and question type, e.g., only incoherent answers to questions of type Others. We will include several variables related to potentially incoherent written answers. Some attributes of these answers can reveal negative moods and attitudes of the student. However, negative affective states help improve the prediction of results in the end-of-year test  Mutation of the k individuals; (4) Cross-over between individuals and the current population; (5) Evaluation and selection of the fittest individual; and (6) Return to (1) and repeat the process. The algorithm starts with 14 regressors. These correspond to the regressors used by the Baseline model. For more details, go to Algorithm A1.Reduction. Two things happen: (a) Too many regressors tend to be selected in relation to the number of regressors in the baseline model, and (b) linear models with more regressors tend to predict the observed variable better. To address this, we reduce the number of regressors selected until we obtain a similar number of regressors as the baseline model. For the Open-ended model, we choose the same number of regressors as in the baseline model to correctly compare the linear models with the Validation. In order to avoid overfitting, we use 250 four-fold cross-validations.The following scheme is used for selecting regressors:Metrics. For regression models, we use two statistical estimators to measure the model fitting. The first is the coefficient of determination or Validation. To validate, the k-fold cross-validation technique is used. This consists of randomly dividing the data set into k chunks of similar sizes. Training and testing with the entire data set allows having an estimate with k samples of the performance of a linear model more representative than an estimate with only one sample when using the classical approach. For the same reason, N random repetitions of k-fold cross-validation are performed; this corresponds to Baseline. To compare the model based on open-ended questions, we propose a single baseline. As with the proposed model, the baseline is a linear regression model. This time, the regressors are the so-called traditional regressors, separated into historical and dynamic regressors. This model consists of 14 regressors: a pre-test, a double regressor corresponding to whether the student is male or female, a single socioeconomic regressor named school vulnerability index (IVE), 2016 SIMCE score for the establishment, and other regressors from the exercises completed during the year. In the work of The following evaluation scheme is proposed to evaluate the model:p-value were obtained using the python scipy.stats module using the pearsonr function .Before showing the Open-ended model with its selected regressors, we will first show other regressors that use the answers to open-ended questions and that were not selected despite having a significant correlation between SIMCE score. First, One of the most important regressors is probably the number of responses, with a correlation of We will now focus on the regressors that use the linguistic attributes of the responses to open-ended questions as an estimator of the SIMCE score. For simplicity, we will report only five of these in wers see . The samstatsmodels python library using the OLS function .Finally, the baseline model and the Open-ended model are subjected to the same evaluation criterion: 250 four-fold cross-validations stratified by establishment. For each model, the average value of the coefficient associated with the regressor and its relevance to the model at each training stage is determined. Both the correlation with the SIMCE score and these two values are reported in one table per model  Exercise accuracy (answers to closed questions), with a correlation of According to The Open-ended model has 14 regressors, just like the baseline model. Of these, only According to i and column j corresponds to the correlation between the i-th regressor and the j-th regressor.Finally, we report the correlation matrix of both models see  and the First, the correlation matrix of the Open-ended model is more homogeneous and null than the that of the baseline model. This means that the Open-ended model regressors correlate less with each other than the baseline model regressors. This is a good feature if we want regressors with different informative estimators per student.Next, the Open-ended model performs better in both training and testing than the baseline model, with an average We have built a set of predictors using written responses in addition to answers to closed questions. We use labels of predictions of question type and coherence type in order to filter the questions and obtain different regressors. Of all these regressors, the Open-ended model mostly uses only regressors based on linguistic attributes (9/14) and only three regressors of the baseline. The other two regressors of the Open-ended model correspond to a simple indicator and a regressor based on traditional attributes from the responses. In addition, all of the regressors selected for this model distinguish between response types according to coherence and/or question type. In particular, these regressors are better estimators for predicting a final score than regressors that do not distinguish between response types. In particular, five of these regressors only use responses that are detected as incoherent, with regressors for question types 1, 2 and 3 being predominant.The baseline model and the model based on open-ended questions only share three of the regressors. The remaining regressors from the baseline model use simple indicators of written answers and estimators constructed using the closed-ended questions. This is not like the model based on open-ended questions, which uses the written answers to these types of questions and linguistic attributes from the answers.Our results also show that the proposed model outperforms the baseline model. The Some of the regressors of the Open-ended model do not contribute significantly to the prediction compared to the rest of the regressors; however, we observed with several experiments that removing them reduces the test performance.We also found models based on open-ended questions with 30 regressors and an expected value of The first one uses the nominal subject dependency tag (nsubj) and the standard deviation as the aggregation function. In our case, the variable “Average: Ratio of dep/nsubj tokens in question-dependent incoherent answers to calculate with explaining questions” considers this type of token but using the average as the aggregation function. In the referenced work The second variable uses the object dependency tag (obj) and the standard deviation as an aggregation function. In our case, the variable “Sum: Number of dep/obj tokens in incoherent answers to choice and/or affirmation questions” considers this type of token but using the sum as the aggregation function. In the referenced work On the other hand, among the retained variables, there is also one that measures the “lexical sophistication” of the students’ writing , which iIn the first experiment, they use a linear model with five linguistic features; two of them are named “Phonographic Neighbors: Function words” and “Word-naming accuracy: Function words”. These variables use the function words (stop-words) from the students’ mails to estimate the self-reported mathematical identity. In the Open-ended model, the variable “Standard deviation: Ratio of stop-word tokens in coherent answers” considers this type of word but using the standard deviation as the aggregation function. In the second experiment, they use a linear model with five linguistic variables; three of them are lexical indices, where one of them is called “Polysemy (adverbs)”. This variable uses the adverbs found in the emails sent and written by the students to predict their math scores. In the Open-ended model, the variable “Maximum: Number of tag/ADV tokens in incoherent answers” considers this type of word but using the maximum as an aggregation function. p-value. Second, we will mention three other variables that are not significant enough for our criteria but deserve a brief mention. We study in order the variables in In the following, we will give an interpretation of a subset of the variables in the Open-ended model. First, the first four most significant variables will be described in detail: that is, those variables with the third quartile (Incoherent answers to type 3 (Choice and/or affirm) questions can be either question-dependent or question-independent incoherent. As the proportion of independent incoherence to this type of questions is significantly less than the dependent ones, without loss of generality, the answers of interest fulfill two properties: (1) they are well structured answers, but (2) they do not answer the question asked. Thus, determining dependency between labels on responses that satisfy (1) and (2) is a potentially important feature.https://universaldependencies.org/u/dep/obj.html, accessed on 16 March 2022). A token with this tag is the phrase that denotes the entity that undergoes the action given by the verb. An example of this type of tag is the following:where tens is the object that undergoes the movement of the verb asked.Now, the dependency tag object (obj) of a verb is the second most important argument of a verb after the subject  he/she answers several times with the obj dependency label or (B) he/she answers few times but in his/her sentences, there is repeatedly the presence of the obj dependency. Note that case (B) can be extended to case (A) by partitioning the long answers into short sub-answers. In any case, the answers have the characteristic of answering the request to “Explain”. They use a structure that attempts to justify a reasoning. However, they do not mention whether the reasoning is correct or not. This characteristic leaves them as incomplete answers that are therefore incoherent.In summary, the variable quantifies incomplete answers to type 3 questions that have an appropriate structure. Therefore, higher values will punish more of those students who do not follow the requested question. Of course, this measure is with respect to the mean value and only for type 3 questions. Since the mean value is https://universaldependencies.org/u/pos/ADV, accessed on 16 March 2022). They can even modify adjectives and other adverbs. An example of this type of tag is the following:where the word very repeatedly modifies the adjective bad.In general, incoherent answers, independent of the question, do not have an appropriate structure, since they are often silent words, sentences without logical meaning and/or non-words. For this reason, the presence of part-of-speech tokens should only be detected on incoherent and sophisticated answers, i.e., question-dependent incoherent answers. Now, the type of token of interest is the adverbial part of speech (ADV). These are words that usually modify verbs in the categories of time, place, direction or mood  and has a positive effect on the model. Therefore, if a student uses several tokens of the ADV type on one of his incoherent answers, he/she will have a directly proportional bonus in the estimation of his/her score. Since the presence of this type of label on sophisticated answers is often used as a discourse embellishment, the bonus is positive when trying to convince with his answer using a stylistic resource.https://universaldependencies.org/u/dep/root.html, accessed on 16 March 2022). Two examples of this type of tag are as follows:where in (1), the only token 23 will have the ROOT tag, while in (2), the token will be find.This time, the responses of interest are incoherent-dependent responses to questions that ask both to calculate a quantity and to justify their answer. For the same reasons as before, the detection of dependency labels in this type of answers is appropriate. Now, the dependency label is ROOT. As the name implies, the ROOT grammatical relation points to the root of the sentence  he/she responds several times to short sentences or (B) few times, but in his/her sentences, there is repeatedly the presence of the ROOT dependency label. Note that case (B) can be extended to case (A) by partitioning the long answers into short sub-answers. In any case, the short answers are characterized by answering the request to “Calculate” but not “Explain”. They answer with only a quantity. However, they do not provide an explanation or justification of how they obtained their result. This characteristic leaves them as incomplete answers that are therefore incoherent. Unlike the longer responses, incomplete responses are penalized more than those that do not provide an amount.Similar to the first variable described, this other variable quantifies incomplete answers to type 2 questions. Thus, higher values will penalize more those students who do not provide an explanation of how they obtained their result. Obviously, this measure is with respect to the mean value and only for type 2 questions. Since the mean value is 3.0, it is to be expected that students who have more than two short answers will further decrease the value of their score.https://universaldependencies.org/u/dep/amod.html, accessed on 16 March 2022). An example of this type of token follows:where drawn is an adjectival modifier of the noun sticks.Unlike the previously described variables, this time, the variable to be analyzed only considers coherent answers to questions of all types. These types of answers are characterized by being well structured and responding to the underlying question. Note that the latter does not mean that the answer is correct. The tags used are adjectival modifier dependency (amod) tags. A token with this tag is any adjectival phrase that serves to modify the noun (or pronoun) , and with a positive effect on the model. Thus, if a student uses several tokens of the amod type on several of his coherent answers, he/she will have a directly proportional bonus in the estimation of his/her score. Since the presence of such labels in sophisticated responses is often used as a discourse ornament, the bonus is positive when adjectives are used as a stylistic device.p-value of the coefficient is not significant. Under our criteria, they are not significant variables. However, it is worth describing them. Some of them have a clear interpretation. For others, their presence may be due to multiple causes that are outside the scope of the analysis in this paper.For the rest of the variables to be analyzed, the third quartile of the Number of coherent answers to choice and/or affirmation questions. This variable is interesting, since it has a positive correlation with the variable to be predicted but contributes a negative effect on the model. This phenomenon is not uncommon. In fact, when working with linear models with more than one variable, there is no reason to expect the sign of the correlation to be the same as that of the model effect. The number of coherent answers has a positive effect but not so much in the type of choice and affirmation questions. Therefore, this regressor corrects for a possible overshoot of the number of coherent responses as accounted in the variable Sum: Ratio of dep/amod tokens in coherent answers.Standard deviation: Ratio of stop-word tokens in coherent answers. Coherent answers are closer to being correct answers than answers that are incoherent. This is because, in particular, coherent responses answer the question appropriately, and incoherent answers do not. For this reason, determining linguistic features on these types of student responses captures structural properties that are not noise. Now, for the variable of interest, we determine the ratio of stop-words over all coherent responses. Stop-words are words that are abound in language. Some of these words are: the, if, what, not, of. Thus, (A) a high ratio of stop-words means that in the answer, there is an abundance of stop-words. Likewise, (B) a low ratio corresponds to a lack of stop-words. It is not clear which (A or B) is better. However, the contribution of this variable in the model suggests that if a student does not consistently follow either style A or B, then this weighs negatively on his/her score. Such consistency is measured by standard deviations. If the standard deviation is far from zero, then this explains an inconsistency of styles.Sum: Number of punctuation symbols in coherent answers to calculate without explaining question. As mentioned for the previous variable, coherent answers are usually correct answers. This time, only coherent answers to questions that ask for a calculation but do not require a justification of how the result was obtained are important. Answers to such questions will be coherent if they at least contain an appropriate mathematical representation, e.g., 3/4. Such mathematical expressions are accompanied by punctuation symbols, e.g., /. In other cases, they serve to explain a procedure to obtain a result, e.g., 1/2 + 2 = 3/2. In this last case, four punctuation symbols are used, twice /, once + and once =. Other students usually represent the same mathematical objects but written in words, e.g., three quarters. This last example does not use punctuation symbols. However, since the variable is a sum of the total number of these punctuation symbols, then all the punctuation symbols used by the student are considered. Thus, students who represent their mathematical objects with symbols will also earn a bonus on their score. In addition, some students often explain using these symbols even though the question does not require them.To the best of our knowledge, this is the first time a study has looked at the contribution of written responses to open-ended questions on weekly online formative assessments when predicting individual performance on end-of-year standardized mathematics tests by students in fourth grade.Students only answered one or two open-ended online questions each week. This is 36.4 times less than the number of answers given to closed questions. Despite this, we found that the written answers provided information that allows us to make better predictions than when only working with responses to close-ended questions. In addition to there being very few open-ended questions when compared to closed questions, the students also wrote very short answers. These low-SES school students are not used to communicating in writing in mathematics. On average, the students wrote only eight to nine words for each answer. This is because they were not used to this type of question, let alone having to explain what they did. Even so, these written answers contain features that allow us to improve our predictions of long-term achievement, as measured by scores on a national summative test. The incredible thing is that this improvement in prediction is achieved even when the summative national tests do not contain any open-ended questions. This just shows the huge potential of open-ended questions and their written responses.On the other hand, there is a growing interest in developing and implementing a dialogic pedagogy : that isIn the near future, it would be important to look at the impact of an implementation with a larger number and proportion of open-ended questions. It would not be surprising if having students write more frequently and justify their responses were to have an even greater effect than that the one reported here. It also remains as future work to investigate how an early and automated warning of incoherence could speed up the process of producing coherent explanations as well as better explanations. On the other hand, it would be interesting to introduce instructions and explicitly teach students different strategies on how to explain their solutions to problems. We could then determine the effect this has on their adoption as well as on the students’ long-term learning. It also remains as future work to study the extent to which this process of written reasoning can be transformed from these early stages to a more rigorous form of mathematical reasoning, leading gradually to the sort of reasoning typically required in mathematical proofs.